survey

A Challenge-based Survey of E-recruitment Recommendation Systems

Authors:

Yoosof Mashayekhi,

Jefrey Lijffijt,

Tijl De BieAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 10

Article No.: 252, Pages 1 - 33

https://doi.org/10.1145/3659942

Published: 22 June 2024 Publication History

Abstract

E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of job seekers for positions and on job seekers’ and recruiters’ preferences. Therefore, e-recruitment recommendation systems may greatly impact people’s careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the competitive edge of companies. Hence, it seems prudent to consider what (unique) challenges there are for recommendation systems in e-recruitment. Existing surveys on this topic discuss past studies from the algorithmic perspective, e.g., by categorizing them into collaborative filtering, content-based, and hybrid methods. This survey, instead, takes a complementary, challenge-based approach. We believe this is more practical for developers facing a concrete e-recruitment design task with a specific set of challenges, and also for researchers that look for impactful research projects in this domain. In this survey, we first identify the main challenges in the e-recruitment recommendation research. Next, we discuss how those challenges have been studied in the literature. Finally, we provide future research directions that we consider most promising in the e-recruitment recommendation domain.

References

[1]

Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. 2020. Multistakeholder recommendation: Survey and research directions. User model. User-adapt. Interact. 30, 1 (2020), 127–158.

[2]

Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2020. Addressing the multistakeholder impact of popularity bias in recommendation through calibration. CoRR abs/2007.12230, (2020). Retrieved from https://arxiv.org/abs/2007.12230

[3]

Fabian Abel, András Benczúr, Daniel Kohlsdorf, Martha Larson, and Róbert Pálovics. 2016. RecSys challenge 2016: Job recommendations. In 10th ACM Conference on Recommender Systems (RecSys’16). Association for Computing Machinery, New York, NY, 425–426. DOI:

Digital Library

[4]

Fabian Abel, Yashar Deldjoo, Mehdi Elahi, and Daniel Kohlsdorf. 2017. RecSys challenge 2017: Offline and online evaluation. In 11th ACM Conference on Recommender Systems (RecSys’17). Association for Computing Machinery, New York, NY, 372–373. DOI:

Digital Library

[5]

Shaha Al-Otaibi and Mourad Ykhlef. 2017. Hybrid immunizing solution for job recommender system. Front. Comput. Sci. 11, 3 (2017), 511–527.

Digital Library

[6]

Nikolaos D. Almalis, George A. Tsihrintzis, and Nikolaos Karagiannis. 2014. A content based approach for recommending personnel for job positions. In 5th International Conference on Information, Intelligence, Systems and Applications (IISA’14). IEEE, 45–49.

[7]

Nikolaos D. Almalis, George A. Tsihrintzis, Nikolaos Karagiannis, and Aggeliki D. Strati. 2015. FoDRA—A new content-based job recommendation algorithm for job seeking and recruiting. In 6th International Conference on Information, Intelligence, Systems and Applications (IISA’15). IEEE, 1–7.

[8]

Suleiman Ali Alsaif, Minyar Sassi Hidri, Hassan Ahmed Eleraky, Imen Ferjani, and Rimah Amami. 2022. Learning-based matched representation system for job recommendation. Computers 11, 11 (2022), 161.

[9]

Suleiman Ali Alsaif, Minyar Sassi Hidri, Imen Ferjani, Hassan Ahmed Eleraky, and Adel Hidri. 2022. NLP-based bi-directional recommendation system: Towards recommending jobs to job seekers and résumé to recruiters. Big Data Cog. Comput. 6, 4 (2022), 147.

[10]

Honorio Apaza, Américo Ariel Rubin de Celis Vidal, and Josimar Edinson Chire Saire. 2021. Job recommendation based on curriculum vitae using text mining. In Future of Information and Communication Conference. Springer, Springer International Publishing, Cham, 1051–1059.

[11]

Adam Mehdi Arafan, David Graus, Fernando P. Santos, and Emma Beauxis-Aussalet. 2022. End-to-end bias mitigation in candidate recommender systems with fairness gates. In 2nd Workshop on Recommender Systems for Human Resources (RecSys-in-HR’22). CEUR-WS.org, 1–8.

[12]

Shoma Arita, Atsushi Hiyama, and Michitaka Hirose. 2017. GBER: A social matching app which utilizes time, place, and skills of workers and jobs. In ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW’17). Association for Computing Machinery, New York, NY, 127–130. DOI:

Digital Library

[13]

Shivam Bansal, Aman Srivastava, and Anuja Arora. 2017. Topic modeling driven content based jobs recommendation engine for recruitment industry. Procedia Comput. Sci. 122 (2017), 865–872.

[14]

Vito Bellini, Giovanni Maria Biancofiore, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci, and Claudio Pomo. 2020. GUapp: A conversational agent for job recommendation for the Italian public administration. In IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS’20). IEEE, 1–7.

[15]

Shuqing Bian, Xu Chen, Wayne Xin Zhao, Kun Zhou, Yupeng Hou, Yang Song, Tao Zhang, and Ji-Rong Wen. 2020. Learning to match jobs with résumé from sparse interaction data using multi-view co-teaching network. In 29th ACM International Conference on Information & Knowledge Management (CIKM’20). Association for Computing Machinery, New York, NY, 65–74. DOI:

Digital Library

[16]

Shuqing Bian, Wayne Xin Zhao, Yang Song, Tao Zhang, and Ji-Rong Wen. 2019. Domain adaptation for person-job fit with transferable deep global match network. In Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19), Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (Eds.). Association for Computational Linguistics, 4810–4820. DOI:

[17]

Mattia Bianchi, Federico Cesaro, Filippo Ciceri, Mattia Dagrada, Alberto Gasparin, Daniele Grattarola, Ilyas Inajjar, Alberto Maria Metelli, and Leonardo Cella. 2017. Content-based approaches for cold-start job recommendations. In Recommender Systems Challenge (RecSys Challenge’17). Association for Computing Machinery, New York, NY, Article 6, 5 pages. DOI:

Digital Library

[18]

Giovanni Maria Biancofiore, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci, and Paolo Pastore. 2021. GUapp: A knowledge-aware conversational agent for job recommendation. In Joint KaRS & ComplexRec Workshop.CEUR-WS.org.

[19]

Giovanni Maria Biancofiore, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci, and Paolo Pastore. 2021. GUapp: Enhancing job recommendations with knowledge graphs. In 11th Italian Information Retrieval Workshop. CEUR-WS.org.

[20]

Guillaume Bied, Solal Nathan, Elia Perennes, Morgane Hoffmann, Philippe Caillou, Bruno Crépon, Christophe Gaillac, and Michèle Sebag. 2023. Toward job recommendation for all. In 32nd International Joint Conference on Artificial Intelligence (IJCAI’23). International Joint Conferences on Artificial Intelligence Organization, 5906–5914. DOI:

Digital Library

[21]

Guillaume Bied, Elia Perennes, Victor Alfonso Naya, Philippe Caillou, Bruno Crépon, Christophe Gaillac, and Michele Sebag. 2021. Congestion-avoiding job recommendation with optimal transport. In FEAST Workshop (ECML-PKDD’21). Retrieved from https://inria.hal.science/hal-03540316

[22]

Joseph Bills and Yiu-kai Dennis Ng. 2021. Looking for jobs? Matching adults with autism with potential employers for job opportunities. In 25th International Database Engineering & Applications Symposium (IDEAS’21). Association for Computing Machinery, New York, NY, 212–221. DOI:

Digital Library

[23]

Ronie C. Bituin, Ronielle B. Antonio, and James A. Esquivel. 2021. Harmonic means between TF-IDF and angle of similarity to identify prospective applicants in a recruitment setting. In 3rd International Conference on Algorithms, Computing and Artificial Intelligence (ACAI’20). Association for Computing Machinery, New York, NY, Article 87, 5 pages. DOI:

Digital Library

[24]

Jacob Bollinger, David Hardtke, and Ben Martin. 2012. Using social data for résumé job matching. In Workshop on Data-driven User Behavioral Modelling and Mining from Social Media (DUBMMSM’12). Association for Computing Machinery, New York, NY, 27–30. DOI:

Digital Library

[25]

Fedor Borisyuk, Krishnaram Kenthapadi, David Stein, and Bo Zhao. 2016. CaSMoS: A framework for learning candidate selection models over structured queries and documents. In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). Association for Computing Machinery, New York, NY, 441–450. DOI:

Digital Library

[26]

Fedor Borisyuk, Liang Zhang, and Krishnaram Kenthapadi. 2017. LiJAR: A system for job application redistribution towards efficient career marketplace. In 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’17). Association for Computing Machinery, New York, NY, 1397–1406. DOI:

Digital Library

[27]

Shayma Boukari, Sondes Fayech, and Rim Faiz. 2020. Huntalent: A candidates recommendation system for automatic recruitment via LinkedIn. In 7th International Conference on Social Networks Analysis, Management and Security (SNAMS’20). IEEE, 1–7.

[28]

Shayma Boukari, Seifeddine Mechti, and Rim Faiz. 2022. A recommendation system for job providers using a big data approach. In Advances in Computational Collective Intelligence, Costin Bădică, Jan Treur, Djamal Benslimane, Bogumiła Hnatkowska, and Marek Krótkiewicz (Eds.). Springer International Publishing, Cham, 57–68.

[29]

Andrei Z. Broder, David Carmel, Michael Herscovici, Aya Soffer, and Jason Zien. 2003. Efficient query evaluation using a two-level retrieval process. In 12th International Conference on Information and Knowledge Management (CIKM’03). Association for Computing Machinery, New York, NY, 426–434. DOI:

Digital Library

[30]

Alan Cardoso, Fernando Mourão, and Leonardo Rocha. 2021. The matching scarcity problem: When recommenders do not connect the edges in recruitment services. Expert Syst. Applic. 175 (2021), 114764.

Digital Library

[31]

Tommaso Carpi, Marco Edemanti, Ervin Kamberoski, Elena Sacchi, Paolo Cremonesi, Roberto Pagano, and Massimo Quadrana. 2016. Multi-stack ensemble for job recommendation. In Recommender Systems Challenge (RecSys Challenge’16). Association for Computing Machinery, New York, NY, Article 8, 4 pages. DOI:

Digital Library

[32]

Sisay Chala and Madjid Fathi. 2017. Job seeker to vacancy matching using social network analysis. In IEEE International Conference on Industrial Technology (ICIT’17). IEEE, 1250–1255.

[33]

Ruey-Cheng Chen, Qingyao Ai, Gaya Jayasinghe, and W. Bruce Croft. 2019. Correcting for recency bias in job recommendation. In 28th ACM International Conference on Information and Knowledge Management (CIKM’19). Association for Computing Machinery, New York, NY, 2185–2188. DOI:

Digital Library

[34]

Wenbo Chen, Pan Zhou, Shaokang Dong, Shimin Gong, Menglan Hu, Kehao Wang, and Dapeng Wu. 2018. Tree-based contextual learning for online job or candidate recommendation with big data support in professional social networks. IEEE Access 6 (2018), 77725–77739.

[35]

Oualid Chenni, Yanis Bouda, Hamid Benachour, and Chahnez Zakaria. 2015. A content-based recommendation approach using semantic user profile in e-recruitment. In Theory and Practice of Natural Computing, Adrian-Horia Dediu, Luis Magdalena, and Carlos Martín-Vide (Eds.). Springer International Publishing, Cham, 23–32.

Digital Library

[36]

S. M. Shawal Chowdhury, Mrithika Chowdhury, and Arifa Sultana. 2023. Matching job circular with résumé using different natural language processing based algorithms. In Machine Intelligence and Emerging Technologies, Md. Shahriare Satu, Mohammad Ali Moni, M. Shamim Kaiser, and Mohammad Shamsul Arefin (Eds.). Springer Nature Switzerland, Cham, 428–442.

[37]

Bruno Coelho, Fernando Costa, and Gil M. Gonçalves. 2015. Hyred: Hybrid job recommendation system. In 12th International Joint Conference on e-Business and Telecommunications (ICETE’15), Vol. 2. IEEE, 29–38.

Digital Library

[38]

Council of European Union. 2022. Proposal for a Regulation of the European Parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts2014. Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1623335154975&uri=CEL EX%3A52021PC0206

[39]

Vachik S. Dave, Baichuan Zhang, Mohammad Al Hasan, Khalifeh AlJadda, and Mohammed Korayem. 2018. A combined representation learning approach for better job and skill recommendation. In 27th ACM International Conference on Information and Knowledge Management (CIKM’18). Association for Computing Machinery, New York, NY, 1997–2005. DOI:

Digital Library

[40]

Toon De Pessemier, Kris Vanhecke, and Luc Martens. 2016. A scalable, high-performance algorithm for hybrid job recommendations. In Recommender Systems Challenge (RecSys Challenge’16). Association for Computing Machinery, New York, NY, Article 5, 4 pages. DOI:

Digital Library

[41]

Corné de Ruijt and Sandjai Bhulai. 2021. Job recommender systems: A review. CoRR abs/2111.13576, (2021). Retrieved from https://arxiv.org/abs/2111.13576

[42]

Mamadou Diaby and Emmanuel Viennet. 2014. Taxonomy-based job recommender systems on Facebook and LinkedIn profiles. In IEEE 8th International Conference on Research Challenges in Information Science (RCIS’14). IEEE, 1–6.

[43]

Mamadou Diaby, Emmanuel Viennet, and Tristan Launay. 2013. Toward the next generation of recruitment tools: An online social network-based job recommender system. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’13). IEEE, 821–828.

Digital Library

[44]

Mamadou Diaby, Emmanuel Viennet, and Tristan Launay. 2014. Exploration of methodologies to improve job recommender systems on social networks. Soc. Netw. Anal. Min. 4, 1 (2014), 1–17.

[45]

Giacomo Domeniconi, Gianluca Moro, Andrea Pagliarani, Karin Pasini, and Roberto Pasolini. 2016. Job recommendation from semantic similarity of LinkedIn users’ skills. In 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM’16), Maria De Marsico, Gabriella Sanniti di Baja, and Ana L. N. Fred (Eds.). SciTePress, 270–277. DOI:

Digital Library

[46]

Shaokang Dong, Zijian Lei, Pan Zhou, Kaigui Bian, and Guanghui Liu. 2017. Job and candidate recommendation with big data support: A contextual online learning approach. In IEEE Global Communications Conference (GLOBECOM’17). IEEE, 1–7.

Digital Library

[47]

Yingpeng Du, Di Luo, Rui Yan, Xiaopei Wang, Hongzhi Liu, Hengshu Zhu, Yang Song, and Jie Zhang. 2024. Enhancing job recommendation through llm-based generative adversarial networks. In Proceedings of the AAAI Conference on Artificial Intelligence, 8363–8371.

[48]

Verena Eitle, Felix Peters, Andreas Welsch, and Peter Buxmann. 2021. The impact of CV recommender systems on procedural justice in recruiting: An experiment in candidate selection. In 29th European Conference on Information Systems - Human Values Crisis in a Digitizing World, ECIS 2021, Marrakech, Morocco, 2020. Retrieved from https://aisel.aisnet.org/ecis2021%5C_rp/65

[49]

Ziad Elgammal, Abdullah Barmu, Hamza Hassan, Khaled Elgammal, Tansel Özyer, and Reda Alhajj. 2021. Matching applicants with positions for better allocation of employees in the job market. In 22nd International Arab Conference on Information Technology (ACIT’21). IEEE, 1–5.

[50]

Ahmed Elsafty, Martin Riedl, and Chris Biemann. 2018. Document-based recommender system for job postings using dense representations. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Srinivas Bangalore, Jennifer Chu-Carroll, and Yunyao Li (Eds.). Association for Computational Linguistics, 216–224. DOI:

[51]

E. P. Ephzibah, R. Sujatha, and Jyotir Moy Chatterjee. 2022. An adaptive neuro-fuzzy inference for blockchain-based smart job recommendation system. Int. J. Inf. Decis. Sci. 14, 1 (2022), 1–14.

[52]

Alessandro Fabris, Nina Baranowska, Matthew J. Dennis, Philipp Hacker, Jorge Saldivar, Frederik J. Zuiderveen Borgesius, and Asia J. Biega. 2023. Fairness and bias in algorithmic hiring. CoRR abs/2309.13933, (2023). DOI:

[53]

Evanthia Faliagka, Lazaros Iliadis, Ioannis Karydis, Maria Rigou, Spyros Sioutas, Athanasios Tsakalidis, and Giannis Tzimas. 2014. On-line consistent ranking on e-recruitment: Seeking the truth behind a well-formed CV. Artif. Intell. Rev. 42, 3 (2014), 515–528.

Digital Library

[54]

Evanthia Faliagka, Athanasios K. Tsakalidis, and Giannis Tzimas. 2012. An integrated e-recruitment system for automated personality mining and applicant ranking. Internet Res. 22, 5 (2012), 551–568. DOI:

[55]

Peini Feng, Charles Jiahao Jiang, Jiale Wang, Sunny Yeung, and Xijie Li. 2021. Job recommendation system based on analytic hierarchy process and k-means clustering. In 13th International Conference on Computer Modeling and Simulation (ICCMS’21). Association for Computing Machinery, New York, NY, 104–113. DOI:

Digital Library

[56]

Francis C. Fernández-Reyes and Suraj Shinde. 2019. CV retrieval system based on job description matching using hybrid word embeddings. Comput. Speech Lang. 56 (2019), 73–79.

Digital Library

[57]

Mauricio Noris Freire and Leandro Nunes de Castro. 2020. A framework for e-recruitment recommender systems. In Artificial Intelligence and Soft Computing, Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, and Jacek M. Zurada (Eds.). Springer International Publishing, Cham, 165–175.

Digital Library

[58]

Mauricio Noris Freire and Leandro Nunes de Castro. 2021. e-recruitment recommender systems: A systematic review. Knowl. Inf. Syst. 63, 1 (2021), 1–20.

Digital Library

[59]

Bin Fu, Hongzhi Liu, Hui Zhao, Yao Zhu, Yang Song, Tao Zhang, and Zhonghai Wu. 2022. Market-aware dynamic person-job fit with hierarchical reinforcement learning. In Database Systems for Advanced Applications, Arnab Bhattacharya, Janice Lee Mong Li, Divyakant Agrawal, P. Krishna Reddy, Mukesh Mohania, Anirban Mondal, Vikram Goyal, and Rage Uday Kiran (Eds.). Springer International Publishing, Cham, 697–705.

Digital Library

[60]

Bin Fu, Hongzhi Liu, Yao Zhu, Yang Song, Tao Zhang, and Zhonghai Wu. 2021. Beyond matching: Modeling two-sided multi-behavioral sequences for dynamic person-job fit. In Database Systems for Advanced Applications, Christian S. Jensen, Ee-Peng Lim, De-Nian Yang, Wang-Chien Lee, Vincent S. Tseng, Vana Kalogeraki, Jen-Wei Huang, and Chih-Ya Shen (Eds.). Springer International Publishing, Cham, 359–375.

Digital Library

[61]

Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. 2021. Advances and challenges in conversational recommender systems: A survey. AI Open 2 (2021), 100–126.

[62]

Gina George and Anisha M. Lal. 2021. A personalized approach to course recommendation in higher education. Int. J. Semant. Web Inf. Syst. 17, 2 (2021), 100–114.

[63]

Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi. 2019. Fairness-aware ranking in search & recommendation systems with application to LinkedIn talent search. In 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD’19). Association for Computing Machinery, New York, NY, 2221–2231. DOI:

Digital Library

[64]

Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, and Krishnaram Kenthapadi. 2018. Talent search and recommendation systems at LinkedIn: Practical challenges and lessons learned. In 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR’18). Association for Computing Machinery, New York, NY, 1353–1354. DOI:

Digital Library

[65]

Aritra Ghosh, Beverly Woolf, Shlomo Zilberstein, and Andrew Lan. 2020. Skill-based career path modeling and recommendation. In IEEE International Conference on Big Data (Big Data’20). IEEE, 1156–1165.

[66]

Jorge Martínez Gil, Bernhard Freudenthaler, and Thomas Natschläger. 2018. Recommendation of job offers using random forests and support vector machines. In Workshops of the EDBT/ICDT Joint Conference (EDBT/ICDT’18)(CEUR Workshop Proceedings, Vol. 2083), Nikolaus Augsten (Ed.). CEUR-WS.org, 22–27. Retrieved from https://ceur-ws.org/Vol-2083/paper-04.pdf

[67]

Alfonso González-Briones, Alberto Rivas, Pablo Chamoso, Roberto Casado-Vara, and Juan Manuel Corchado. 2019. Case-based reasoning and agent based job offer recommender system. In 13th International Joint Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO’18-CISIS’18-ICEUTE’18), Manuel Graña, José Manuel López-Guede, Oier Etxaniz, Álvaro Herrero, José Antonio Sáez, Héctor Quintián, and Emilio Corchado (Eds.). Springer International Publishing, Cham, 21–33.

[68]

Akshay Gugnani and Hemant Misra. 2020. Implicit skills extraction using document embedding and its use in job recommendation. In 34th AAAI Conference on Artificial Intelligence (AAAI’20), 32nd Innovative Applications of Artificial Intelligence Conference (IAAI’20), 10th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI’20. AAAI Press, 13286–13293. DOI:

[69]

Cheng Guo, Hongyu Lu, Shaoyun Shi, Bin Hao, Bin Liu, Min Zhang, Yiqun Liu, and Shaoping Ma. 2017. How integration helps on cold-start recommendations. In Recommender Systems Challenge (RecSys Challenge’17). Association for Computing Machinery, New York, NY, Article 1, 6 pages. DOI:

Digital Library

[70]

Shiqiang Guo, Folami Alamudun, and Tracy Hammond. 2016. RésuMatcher: A personalized résumé-job matching system. Expert Syst. Applic. 60 (2016), 169–182.

Digital Library

[71]

Anika Gupta and Deepak Garg. 2014. Applying data mining techniques in job recommender system for considering candidate job preferences. In International Conference on Advances in Computing, Communications and Informatics (ICACCI’14). IEEE, 1458–1465.

[72]

Francisco Gutiérrez, Sven Charleer, Robin De Croon, Nyi Nyi Htun, Gerd Goetschalckx, and Katrien Verbert. 2019. Explaining and exploring job recommendations: A user-driven approach for interacting with knowledge-based job recommender systems. In 13th ACM Conference on Recommender Systems (RecSys’19). Association for Computing Machinery, New York, NY, 60–68. DOI:

Digital Library

[73]

Amine Habous and El Habib Nfaoui. 2021. A fuzzy logic and ontology-based approach for improving the CV and job offer matching in recruitment process. Int. J. Metadata, Semant. Ontol. 15, 2 (2021), 104–120.

Digital Library

[74]

Claudia Hauff and Georgios Gousios. 2015. Matching GitHub developer profiles to job advertisements. In IEEE/ACM 12th Working Conference on Mining Software Repositories. IEEE, 362–366.

Digital Library

[75]

Miao He, Dayong Shen, Tao Wang, Hua Zhao, Zhongshan Zhang, and Renjie He. 2023. Self-attentional multi-field features representation and interaction learning for person–job fit. IEEE Trans. Computat. Soc. Syst. 10, 1 (2023), 255–268. DOI:

[76]

Miao He, Tao Wang, Yuanyuan Zhu, Yingguo Chen, Feng Yao, and Ning Wang. 2021. FINN: Feature interaction neural network for person-job fit. In 7th International Conference on Big Data and Information Analytics (BigDIA’21). IEEE, 123–130.

[77]

Bradford Heap, Alfred Krzywicki, Wayne Wobcke, Mike Bain, and Paul Compton. 2014. Combining career progression and profile matching in a job recommender system. In Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence (PRICAI’14), Duc-Nghia Pham and Seong-Bae Park (Eds.). Springer International Publishing, Cham, 396–408.

[78]

Islam A. Heggo and Nashwa Abdelbaki. 2018. Hybrid information filtering engine for personalized job recommender system. In International Conference on Advanced Machine Learning Technologies and Applications (AMLTA’18), Aboul Ella Hassanien, Mohamed F. Tolba, Mohamed Elhoseny, and Mohamed Mostafa (Eds.). Springer International Publishing, Cham, 553–563.

[79]

Wenxing Hong, Siting Zheng, and Huan Wang. 2013. Dynamic user profile-based job recommender system. In 8th International Conference on Computer Science & Education. IEEE, 1499–1503.

[80]

Wenxing Hong, Siting Zheng, Huan Wang, and Jianchao Shi. 2013. A job recommender system based on user clustering. J. Comput. 8, 8 (2013), 1960–1967.

[81]

Yupeng Hou, Xingyu Pan, Wayne Xin Zhao, Shuqing Bian, Yang Song, Tao Zhang, and Ji-Rong Wen. 2022. Leveraging search history for improving person-job fit. In Database Systems for Advanced Applications, Arnab Bhattacharya, Janice Lee Mong Li, Divyakant Agrawal, P. Krishna Reddy, Mukesh Mohania, Anirban Mondal, Vikram Goyal, and Rage Uday Kiran (Eds.). Springer International Publishing, Cham, 38–54.

Digital Library

[82]

Xiao Hu, Yuan Cheng, Zhi Zheng, Yue Wang, Xinxin Chi, and Hengshu Zhu. 2023. BOSS: A bilateral occupational-suitability-aware recommender system for online recruitment. In 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’23). Association for Computing Machinery, New York, NY, 4146–4155. DOI:

Digital Library

[83]

Rashidul Islam, Kamrun Naher Keya, Ziqian Zeng, Shimei Pan, and James Foulds. 2021. Debiasing career recommendations with neural fair collaborative filtering. In the Web Conference (WWW’21). Association for Computing Machinery, New York, NY, 3779–3790. DOI:

Digital Library

[84]

Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021. A survey on conversational recommender systems. ACM Comput. Surv. 54, 5 (2021), 1–36.

Digital Library

[85]

Junshu Jiang, Songyun Ye, Wei Wang, Jingran Xu, and Xiaosheng Luo. 2020. Learning effective representations for person-job fit by feature fusion. In 29th ACM International Conference on Information & Knowledge Management (CIKM’20). Association for Computing Machinery, New York, NY, 2549–2556. DOI:

Digital Library

[86]

Miao Jiang, Yi Fang, Huangming Xie, Jike Chong, and Meng Meng. 2019. User click prediction for personalized job recommendation. World Wide Web 22, 1 (2019), 325–345.

Digital Library

[87]

Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019. Billion-scale similarity search with gpus. IEEE Trans. Big Data 7, 3 (2019), 535–547.

[88]

Atakan Kara, F. Serhan Daniş, Günce K. Orman, Sultan N. Turhan, and Ö. Anıl Özlü. 2023. Job recommendation based on extracted skill embeddings. In Intelligent Systems and Applications, Kohei Arai (Ed.). Springer International Publishing, Cham, 497–507.

[89]

Krishnaram Kenthapadi, Benjamin Le, and Ganesh Venkataraman. 2017. Personalized job recommendation system at LinkedIn: Practical challenges and lessons learned. In 11th ACM Conference on Recommender Systems (RecSys’17). Association for Computing Machinery, New York, NY, 346–347. DOI:

Digital Library

[90]

Aparup Khatua and Wolfgang Nejdl. 2020. Matching recruiters and jobseekers on Twitter. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’20). IEEE, 266–269.

Digital Library

[91]

Robert Kwieciński, Grzegorz Melniczak, and Tomasz Górecki. 2023. Comparison of real-time and batch job recommendations. IEEE Access 11 (2023), 20553–20559.

[92]

Emanuel Lacic, Markus Reiter-Haas, Tomislav Duricic, Valentin Slawicek, and Elisabeth Lex. 2019. Should we embed? A study on the online performance of utilizing embeddings for real-time job recommendations. In 13th ACM Conference on Recommender Systems (RecSys’19). Association for Computing Machinery, New York, NY, 496–500. DOI:

Digital Library

[93]

Emanuel Lacic, Markus Reiter-Haas, Dominik Kowald, Manoj Reddy Dareddy, Junghoo Cho, and Elisabeth Lex. 2020. Using autoencoders for session-based job recommendations. User model. User-adapt. Interact. 30, 4 (2020), 617–658.

[94]

Dor Lavi, Volodymyr Medentsiy, and David Graus. 2021. conSultantBERT: Fine-tuned Siamese sentence-BERT for matching jobs and job seekers. In Workshop on Recommender Systems for Human Resources (RecSys in HR’21) co-located with the 15th ACM Conference on Recommender Systems (RecSys’21)(CEUR Workshop Proceedings, Vol. 2967), Mesut Kaya, Toine Bogers, David Graus, Katrien Verbert, and Francisco Gutiérrez (Eds.). CEUR-WS.org. Retrieved from http://ceur-ws.org/Vol-2967/paper_8.pdf

[95]

Ran Le, Wenpeng Hu, Yang Song, Tao Zhang, Dongyan Zhao, and Rui Yan. 2019. Towards effective and interpretable person-job fitting. In 28th ACM International Conference on Information and Knowledge Management (CIKM’19). Association for Computing Machinery, New York, NY, 1883–1892. DOI:

Digital Library

[96]

Yeon-Chang Lee, Jiwon Hong, and Sang-Wook Kim. 2016. Job recommendation in AskStory: Experiences, methods, and evaluation. In 31st Annual ACM Symposium on Applied Computing (SAC’16). Association for Computing Machinery, New York, NY, 780–786. DOI:

Digital Library

[97]

Vasily Leksin and Andrey Ostapets. 2016. Job recommendation based on factorization machine and topic modelling. In Recommender Systems Challenge (RecSys Challenge’16). Association for Computing Machinery, New York, NY, Article 6, 4 pages. DOI:

Digital Library

[98]

Changmao Li, Elaine Fisher, Rebecca Thomas, Steve Pittard, Vicki Hertzberg, and Jinho D. Choi. 2020. Competence-level prediction and résumé & job description matching using context-aware transformer models. In Conference on Empirical Methods in Natural Language Processing (EMNLP’20), Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, 8456–8466. DOI:

[99]

Nan Li, Bo Kang, Jefrey Lijffijt, and Tijl De Bie. 2024. FEIR: Quantifying and reducing envy and inferiority for fair recommendation of limited resources. ACM Trans. Intell. Syst. Technol. (Feb.2024). DOI:Just Accepted.

Digital Library

[100]

Pu Li, Tianci Li, Xin Wang, Suzhi Zhang, Yuncheng Jiang, and Yong Tang. 2022. Scholar recommendation based on high-order propagation of knowledge graphs. Int. J. Semant. Web Inf. Syst. 18, 1 (2022), 1–19.

Digital Library

[101]

Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang. 2022. Fairness in recommendation: A survey. CoRR abs/2205.13619, (2022). DOI:

[102]

Jianxun Lian, Fuzheng Zhang, Min Hou, Hongwei Wang, Xing Xie, and Guangzhong Sun. 2017. Practical lessons for job recommendations in the cold-start scenario. In Recommender Systems Challenge (RecSys Challenge’17). Association for Computing Machinery, New York, NY, Article 4, 6 pages. DOI:

Digital Library

[103]

Yiou Lin, Hang Lei, Prince Clement Addo, and Xiaoyu Li. 2016. Machine learned résumé-job matching solution. CoRR abs/1607.07657, (2016). Retrieved from http://arxiv.org/abs/1607.07657

[104]

Kuan Liu, Xing Shi, Anoop Kumar, Linhong Zhu, and Prem Natarajan. 2016. Temporal learning and sequence modeling for a job recommender system. In Recommender Systems Challenge (RecSys Challenge’16). Association for Computing Machinery, New York, NY, Article 7, 4 pages. DOI:

Digital Library

[105]

Mengshu Liu, Jingya Wang, Kareem Abdelfatah, and Mohammed Korayem. 2019. Tripartite vector representations for better job recommendation. In 1st International Workshop on Challenges and Experiences from Data Integration to Knowledge Graphs co-located with the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD’19)(CEUR Workshop Proceedings, Vol. 2512), Donatella Firmani, Valter Crescenzi, Andrea De Angelis, Xin Luna Dong, Maurizio Mazzei, Paolo Merialdo, and Divesh Srivastava (Eds.). CEUR-WS.org. Retrieved from https://ceur-ws.org/Vol-2512/paper2.pdf

[106]

Rui Liu, Yuanxin Ouyang, Wenge Rong, Xin Song, Cui Tang, and Zhang Xiong. 2016. Rating prediction based job recommendation service for college students. In International Conference on Computational Science and Its Applications (ICCSA’16), Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Ana Maria A. C. Rocha, Carmelo M. Torre, David Taniar, Bernady O. Apduhan, Elena Stankova, and Shangguang Wang (Eds.). Springer International Publishing, Cham, 453–467.

[107]

Rui Liu, Wenge Rong, Yuanxin Ouyang, and Zhang Xiong. 2017. A hierarchical similarity based job recommendation service framework for university students. Front. Comput. Sci. 11, 5 (2017), 912–922.

Digital Library

[108]

Yao Lu, Sandy El Helou, and Denis Gillet. 2013. A recommender system for job seeking and recruiting website. In 22nd International Conference on World Wide Web (WWW’13). Association for Computing Machinery, New York, NY, 963–966. DOI:

Digital Library

[109]

Yong Luo, Huaizheng Zhang, Yonggang Wen, and Xinwen Zhang. 2019. ResumeGAN: An optimized deep representation learning framework for talent-job fit via adversarial learning. In 28th ACM International Conference on Information and Knowledge Management (CIKM’19). Association for Computing Machinery, New York, NY, 1101–1110. DOI:

Digital Library

[110]

Saket Maheshwary and Hemant Misra. 2018. Matching résumé to jobs via deep Siamese network. In the Web Conference (WWW’18). International World Wide Web Conferences Steering Committee, 87–88. DOI:

Digital Library

[111]

Emmanuel Malherbe, Mamadou Diaby, Mario Cataldi, Emmanuel Viennet, and Marie-Aude Aufaure. 2014. Field selection for job categorization and recommendation to social network users. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’14). IEEE, 588–595.

[112]

Mohammed Maree, Aseel B. Kmail, and Mohammed Belkhatir. 2019. Analysis and shortcomings of e-recruitment systems: Towards a semantics-based approach addressing knowledge incompleteness and limited domain coverage. J. Inf. Sci. 45, 6 (2019), 713–735.

Digital Library

[113]

Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt, and Tijl De Bie. 2023. ReCon: Reducing congestion in job recommendation using optimal transport. In 17th ACM Conference on Recommender Systems (RecSys’23). Association for Computing Machinery, New York, NY, 696–701. DOI:

Digital Library

[114]

Mohamed Amine Menacer, Fatma Ben Hamda, Ghada Mighri, Sabeur Ben Hamidene, and Maxime Cariou. 2021. An interpretable person-job fitting approach based on classification and ranking. In 4th International Conference on Natural Language and Speech Processing, Mourad Abbas and Abed Alhakim Freihat (Eds.). Association for Computational Linguistics, 278–286. Retrieved from: https://aclanthology.org/2021.icnlsp-1.15

[115]

François Mentec, Zoltán Miklós, Sebastien Hervieu, and Thierry Roger. 2021. Conversational recommendations for job recruiters (long paper). In Joint Workshop of the 3rd Edition of Knowledge-aware and Conversational Recommender Systems (KaRS’21) and the 5th Edition of Recommendation in Complex Environments (ComplexRec’21) co-located with 15th ACM Conference on Recommender Systems (RecSys’21)(CEUR Workshop Proceedings, Vol. 2960), Vito Walter Anelli, Pierpaolo Basile, Tommaso Di Noia, Francesco Maria Donini, Cataldo Musto, Fedelucio Narducci, Markus Zanker, Himan Abdollahpouri, Toine Bogers, Bamshad Mobasher, Casper Petersen, and Maria Soledad Pera (Eds.). CEUR-WS.org. Retrieved from https://ceur-ws.org/Vol-2960/paper17.pdf

[116]

D. Mhamdi, Reda Moulouki, Mohammed Yassine El Ghoumari, M. Azzouazi, and L. Moussaid. 2020. Job recommendation based on job profile clustering and job seeker behavior. Procedia Comput. Sci. 175 (2020), 695–699.

[117]

Tsunenori Mine, Tomoyuki Kakuta, and Akira Ono. 2013. Reciprocal recommendation for job matching with bidirectional feedback. In 2nd IIAI International Conference on Advanced Applied Informatics. IEEE, 39–44.

Digital Library

[118]

Abdul Hanan Minhas, Mohammad Daniyal Shaiq, Saad Ali Qureshi, Musa Dildar Ahmed Cheema, Shujaat Hussain, and Kifayat Ullah Khan. 2022. An efficient algorithm for ranking candidates in e-recruitment system. In 16th International Conference on Ubiquitous Information Management and Communication (IMCOM’22). IEEE, 1–8.

[119]

Sonu K. Mishra and Manoj Reddy. 2016. A bottom-up approach to job recommendation system. In Recommender Systems Challenge (RecSys Challenge’16). Association for Computing Machinery, New York, NY, Article 4, 4 pages. DOI:

Digital Library

[120]

Ala Mughaid, Ibrahim Obeidat, Bilal Hawashin, Shadi AlZu’bi, and Darah Aqel. 2019. A smart geo-location job recommender system based on social media posts. In 6th International Conference on Social Networks Analysis, Management and Security (SNAMS’19). IEEE, 505–510.

[121]

Amber Nigam, Aakash Roy, Hartaran Singh, and Harsimran Waila. 2019. Job recommendation through progression of job selection. In IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS’19). IEEE, 212–216.

[122]

Dimos Ntioudis, Panagiota Masa, Anastasios Karakostas, Georgios Meditskos, Stefanos Vrochidis, and Ioannis Kompatsiaris. 2022. Ontology-based personalized job recommendation framework for migrants and refugees. Big Data Cog. Comput. 6, 4 (2022), 120.

[123]

Andrzej Pacuk, Piotr Sankowski, Karol Wundefinedgrzycki, Adam Witkowski, and Piotr Wygocki. 2016. RecSys challenge 2016: Job recommendations based on preselection of offers and gradient boosting. In Recommender Systems Challenge (RecSys Challenge’16). Association for Computing Machinery, New York, NY, Article 10, 4 pages. DOI:

Digital Library

[124]

Ioannis Paparrizos, B. Barla Cambazoglu, and Aristides Gionis. 2011. Machine learned job recommendation. In 5th ACM Conference on Recommender Systems (RecSys’11). Association for Computing Machinery, New York, NY, 325–328. DOI:

Digital Library

[125]

Bharat Patel, Varun Kakuste, and Magdalini Eirinaki. 2017. CaPaR: A career path recommendation framework. In IEEE 3rd International Conference on Big Data Computing Service and Applications (BigDataService’17). IEEE, 23–30.

[126]

Vishnu S. Pendyala, Nishtha Atrey, Tina Aggarwal, and Saumya Goyal. 2022. Enhanced algorithmic job matching based on a comprehensive candidate profile using NLP and machine learning. In IEEE 8th International Conference on Big Data Computing Service and Applications (BigDataService’22). IEEE, 183–184.

[127]

Mirko Polato and Fabio Aiolli. 2016. A preliminary study on a recommender system for the job recommendation challenge. In Recommender Systems Challenge (RecSys Challenge’16). Association for Computing Machinery, New York, NY, Article 1, 4 pages. DOI:

Digital Library

[128]

Chuan Qin, Hengshu Zhu, Tong Xu, Chen Zhu, Liang Jiang, Enhong Chen, and Hui Xiong. 2018. Enhancing person-job fit for talent recruitment: An ability-aware neural network approach. In 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR’18). Association for Computing Machinery, New York, NY, 25–34. DOI:

Digital Library

[129]

Chuan Qin, Hengshu Zhu, Tong Xu, Chen Zhu, Chao Ma, Enhong Chen, and Hui Xiong. 2020. An enhanced neural network approach to person-job fit in talent recruitment. ACM Trans. Inf. Syst. 38, 2 (2020), 1–33.

Digital Library

[130]

Gábor Rácz, Attila Sali, and Klaus-Dieter Schewe. 2016. Semantic matching strategies for job recruitment: A comparison of new and known approaches. In Foundations of Information and Knowledge Systems, Marc Gyssens and Guillermo Simari (Eds.). Springer International Publishing, Cham, 149–168.

Digital Library

[131]

Manish Raghavan, Solon Barocas, Jon Kleinberg, and Karen Levy. 2020. Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Conference on Fairness, Accountability, and Transparency (FAT*’20). Association for Computing Machinery, New York, NY, 469–481. DOI:

Digital Library

[132]

Michael Reusens, Wilfried Lemahieu, Bart Baesens, and Luc Sels. 2017. A note on explicit versus implicit information for job recommendation. Decis. Supp. Syst. 98 (2017), 26–35.

Digital Library

[133]

Sima Rezaeipourfarsangi and Evangelos E. Milios. 2023. AI-powered résumé-job matching: A document ranking approach using deep neural networks. In ACM Symposium on Document Engineering (DocEng’23). Association for Computing Machinery, New York, NY, Article 22, 4 pages. DOI:

Digital Library

[134]

Alberto Rivas, Pablo Chamoso, Alfonso González-Briones, Roberto Casado-Vara, and Juan Manuel Corchado. 2019. Hybrid job offer recommender system in a social network. Expert Syst. 36, 4 (2019), e12416.

[135]

Leah G. Rodriguez and Enrico P. Chavez. 2019. Feature selection for job matching application using profile matching model. In IEEE 4th International Conference on Computer and Communication Systems (ICCCS’19). IEEE, 263–266.

[136]

Pradeep Kumar Roy, Sarabjeet Singh Chowdhary, and Rocky Bhatia. 2020. A machine learning approach for automation of résumé recommendation system. Procedia Comput. Sci. 167 (2020), 2318–2327.

[137]

Clara Rus, Jeffrey Luppes, Harrie Oosterhuis, and Gido H. Schoenmacker. 2022. Closing the gender wage gap: Adversarial fairness in job recommendation. In 2nd Workshop on Recommender Systems for Human Resources (RecSys-in-HR’22) co-located with the 16th ACM Conference on Recommender Systems (RecSys’22)(CEUR Workshop Proceedings, Vol. 3218), Mesut Kaya, Toine Bogers, David Graus, Sepideh Mesbah, Chris Johnson, and Francisco Gutiérrez (Eds.). CEUR-WS.org. Retrieved from http://ceur-ws.org/Vol-3218/RecSysHR2022-paper_3.pdf

[138]

Yosuke Saito and Kazunari Sugiyama. 2022. Job recommendation based on multiple behaviors and explicit preferences. In IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’22). IEEE, 1–8.

[139]

Oscar M. Salazar, Juan C. Jaramillo, Demetrio A. Ovalle, and Jaime A. Guzmán. 2015. A case-based multi-agent and recommendation environment to improve the e-recruitment process. In Highlights of Practical Applications of Agents, Multi-agent Systems, and Sustainability - The PAAMS Collection, Javier Bajo, Kasper Hallenborg, Pawel Pawlewski, Vicente Botti, Nayat Sánchez-Pi, Nestor Darío Duque Méndez, Fernando Lopes, and Vicente Julian (Eds.). Springer International Publishing, Cham, 389–397.

[140]

George Salloum and Joe Tekli. 2021. Automated and personalized nutrition health assessment, recommendation, and progress evaluation using fuzzy reasoning. Int. J. Hum.-comput. Stud. 151 (2021), 102610.

[141]

Javier Sánchez-Monedero, Lina Dencik, and Lilian Edwards. 2020. What does it mean to “solve” the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems. In Conference on Fairness, Accountability, and Transparency (FAT*’20). Association for Computing Machinery, New York, NY, 458–468. DOI:

Digital Library

[142]

Masahiro Sato, Koki Nagatani, and Takuji Tahara. 2017. Exploring an optimal online model for new job recommendation: Solution for RecSys challenge 2017. In Recommender Systems Challenge (RecSys Challenge’17). Association for Computing Machinery, New York, NY, Article 5, 5 pages. DOI:

Digital Library

[143]

Thomas Schmitt, Phillipe Caillou, and Michèle Sebag. 2016. Matching jobs and résumé: A deep collaborative filtering task. In 2nd Global Conference on Artificial Intelligence (GCAI’16),(EPiC Series in Computing, Vol. 41), Christoph Benzmüller, Geoff Sutcliffe, and Raúl Rojas (Eds.). EasyChair, 124–137. DOI:

[144]

Thomas Schmitt, François Gonard, Philippe Caillou, and Michèle Sebag. 2017. Language modelling for collaborative filtering: Application to job applicant matching. In IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI’17). IEEE, 1226–1233.

[145]

Walid Shalaby, BahaaEddin AlAila, Mohammed Korayem, Layla Pournajaf, Khalifeh AlJadda, Shannon Quinn, and Wlodek Zadrozny. 2017. Help me find a job: A graph-based approach for job recommendation at scale. In IEEE International Conference on Big Data (Big Data’17). IEEE, 1544–1553.

[146]

Baoxu Shi, Jaewon Yang, Feng Guo, and Qi He. 2020. Salience and market-aware skill extraction for job targeting. In 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD’20). Association for Computing Machinery, New York, NY, 2871–2879. DOI:

Digital Library

[147]

Xiaowei Shi, Jiarun Song, Junchao Wu, and Qiang Wei. 2023. Serialized knowledge enhanced multi-objective person-job matching recommendation in a high mobility job market. In 56th Hawaii International Conference on System Sciences (HICSS’23), Tung X. Bui (Ed.). ScholarSpace, 980–989. Retrieved from https://hdl.handle.net/10125/102750

[148]

Saman Shishehchi and Seyed Yashar Banihashem. 2019. JRDP: A job recommender system based on ontology for disabled people. Int. J. Technol. Hum. Interact. 15, 1 (2019), 85–99.

Digital Library

[149]

Olfa Slama and Patrice Darmon. 2021. A novel personalized preference-based approach for job/candidate recommendation. In Research Challenges in Information Science, Samira Cherfi, Anna Perini, and Selmin Nurcan (Eds.). Springer International Publishing, Cham, 418–434.

[150]

Ellery Smith, Andreas Weiler, and Martin Braschler. 2021. Skill extraction for domain-specific text retrieval in a job-matching platform. In Experimental IR Meets Multilinguality, Multimodality, and Interaction, K. Selçuk Candan, Bogdan Ionescu, Lorraine Goeuriot, Birger Larsen, Henning Müller, Alexis Joly, Maria Maistro, Florina Piroi, Guglielmo Faggioli, and Nicola Ferro (Eds.). Springer International Publishing, Cham, 116–128.

Digital Library

[151]

Nguyen Dinh Thuan, Nguyen Minh Nhut, Dang Minh Quan, and Le Mai Duy Khanh. 2022. Using blockchain and artificial intelligence to build a job recommendation system for students in information technology. In RIVF International Conference on Computing and Communication Technologies (RIVF’22). IEEE, 364–369.

[152]

Chirayu Upadhyay, Hasan Abu-Rasheed, Christian Weber, and Madjid Fathi. 2021. Explainable job-posting recommendations using knowledge graphs and named entity recognition. In IEEE International Conference on Systems, Man, and Cybernetics (SMC’21). IEEE, 3291–3296.

Digital Library

[153]

Jorge Carlos Valverde-Rebaza, Ricardo Puma, Paul Bustios, and Nathalia C. Silva. 2018. Job recommendation based on job seeker skills: An empirical study. In 1st Workshop on Narrative Extraction From Text (Text2Story’18) Co-located with 40th European Conference on Information Retrieval (ECIR’18)(CEUR Workshop Proceedings, Vol. 2077), Alípio Mário Jorge, Ricardo Campos, Adam Jatowt, and Sérgio Nunes (Eds.). CEUR-WS.org, 47–51. Retrieved from https://ceur-ws.org/Vol-2077/paper6.pdf

[154]

Maksims Volkovs, Guang Wei Yu, and Tomi Poutanen. 2017. Content-based neighbor models for cold start in recommender systems. In Recommender Systems Challenge (RecSys Challenge’17). Association for Computing Machinery, New York, NY, Article 7, 6 pages. DOI:

Digital Library

[155]

Clarice Wang, Kathryn Wang, Andrew Bian, Rashidul Islam, Kamrun Naher Keya, James Foulds, and Shimei Pan. 2022. Do humans prefer debiased AI algorithms? A case study in career recommendation. In 27th International Conference on Intelligent User Interfaces (IUI’22). Association for Computing Machinery, New York, NY, 134–147. DOI:

Digital Library

[156]

Lei Wang, Yuanyuan Fu, and Yingchao Zhang. 2023. A career recommendation method for college students based on occupational values. Int. J. Emerg. Technol. Learn. 18, 1 (2023), 201.

[157]

Xiaowei Wang, Zhenhong Jiang, and Lingxi Peng. 2021. A deep-learning-inspired person-job matching model based on sentence vectors and subject-term graphs. Complex 2021 (2021), 6206288:1–6206288:11. DOI:

Digital Library

[158]

Yusen Wang, Kaize Shi, and Zhendong Niu. 2020. A session-based job recommendation system combining area knowledge and interest graph neural networks. In 32nd International Conference on Software Engineering and Knowledge Engineering (SEKE’20), Raúl García-Castro (Ed.). KSI Research Inc., 489–492. DOI:

[159]

Ziyang Wang, Wei Wei, Chenwei Xu, Jun Xu, and Xian-Ling Mao. 2022. Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networks. Neurocomputing 501 (2022), 14–24.

Digital Library

[160]

Likang Wu, Zhaopeng Qiu, Zhi Zheng, Hengshu Zhu, and Enhong Chen. 2024. Exploring large language model for graph data understanding in online job recommendations. In Proceedings of the AAAI Conference on Artificial Intelligence, 9178–9186.

[161]

Jianmao Xiao, Xinyi Liu, Jia Zeng, Yuanlong Cao, and Zhiyong Feng. 2022. Recommendation of healthcare services based on an embedded user profile model. Int. J. Semant. Web Inf. Syst. 18, 1 (2022), 1–21.

Digital Library

[162]

Wenming Xiao, Xiao Xu, Kang Liang, Junkang Mao, and Jun Wang. 2016. Job recommendation with Hawkes process: An effective solution for RecSys challenge 2016. In Recommender Systems Challenge (RecSys Challenge’16). Association for Computing Machinery, New York, NY, Article 11, 4 pages. DOI:

Digital Library

[163]

Peng Xu and Denilson Barbosa. 2018. Matching résumés to job descriptions with stacked models. In Advances in Artificial Intelligence, Ebrahim Bagheri and Jackie C. K. Cheung (Eds.). Springer International Publishing, Cham, 304–309.

[164]

Murat Yagci and Fikret Gurgen. 2017. A ranker ensemble for multi-objective job recommendation in an item cold start setting. In Recommender Systems Challenge (RecSys Challenge’17). Association for Computing Machinery, New York, NY, Article 2, 4 pages. DOI:

Digital Library

[165]

Rui Yan, Ran Le, Yang Song, Tao Zhang, Xiangliang Zhang, and Dongyan Zhao. 2019. Interview choice reveals your preference on the market: To improve job-résumé matching through profiling memories. In 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD’19). Association for Computing Machinery, New York, NY, 914–922. DOI:

Digital Library

[166]

Chen Yang, Yupeng Hou, Yang Song, Tao Zhang, Ji-Rong Wen, and Wayne Xin Zhao. 2022. Modeling two-way selection preference for person-job fit. In 16th ACM Conference on Recommender Systems (RecSys’22). Association for Computing Machinery, New York, NY, 102–112. DOI:

Digital Library

[167]

Shuo Yang, Mohammed Korayem, Khalifeh AlJadda, Trey Grainger, and Sriraam Natarajan. 2017. Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive statistical relational learning approach. Knowl.-based Syst. 136 (2017), 37–45.

Digital Library

[168]

Zhe-Rui Yang, Zhen-Yu He, Chang-Dong Wang, Pei-Yuan Lai, De-Zhang Liao, and Zhong-Zheng Wang. 2022. A bi-directional recommender system for online recruitment. In IEEE International Conference on Data Mining (ICDM’22). IEEE, 628–637.

[169]

Kaichun Yao, Jingshuai Zhang, Chuan Qin, Peng Wang, Hengshu Zhu, and Hui Xiong. 2022. Knowledge enhanced person-job fit for talent recruitment. In IEEE 38th International Conference on Data Engineering (ICDE’22). IEEE, 3467–3480.

[170]

Chenrui Zhang and Xueqi Cheng. 2016. An ensemble method for job recommender systems. In Recommender Systems Challenge (RecSys Challenge’16). Association for Computing Machinery, New York, NY, Article 2, 4 pages. DOI:

Digital Library

[171]

XianXing Zhang, Yitong Zhou, Yiming Ma, Bee-Chung Chen, Liang Zhang, and Deepak Agarwal. 2016. GLMix: Generalized linear mixed models for large-scale response prediction. In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). Association for Computing Machinery, New York, NY, 363–372. DOI:

Digital Library

[172]

Yunchong Zhang, Baisong Liu, Jiangbo Qian, Jiangcheng Qin, Xueyuan Zhang, and Xueyong Jiang. 2021. An explainable person-job fit model incorporating structured information. In IEEE International Conference on Big Data (Big Data’21). IEEE, 3571–3579.

[173]

Jing Zhao, Jingya Wang, Madhav Sigdel, Bopeng Zhang, Phuong Hoang, Mengshu Liu, and Mohammed Korayem. 2021. Embedding-based recommender system for job to candidate matching on scale. CoRR abs/2107.00221, (2021). Retrieved from https://arxiv.org/abs/2107.00221

[174]

Tianhua Zhao, Cheng Wuyu, and Chen Zhixiang. 2021. Summer job selection model based on job matching and comprehensive evaluation algorithm. In 2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS’21). Association for Computing Machinery, New York, NY, Article 187, 5 pages. DOI:

Digital Library

[175]

Zhi Zheng, Zhaopeng Qiu, Xiao Hu, Likang Wu, Hengshu Zhu, and Hui Xiong. 2023. Generative job recommendations with large language model. CoRR abs/2307.02157, (2023). DOI:

[176]

Chen Zhu, Hengshu Zhu, Hui Xiong, Chao Ma, Fang Xie, Pengliang Ding, and Pan Li. 2018. Person-job fit: Adapting the right talent for the right job with joint representation learning. ACM Trans. Manag. Inf. Syst. 9, 3 (2018), 1–17.

Digital Library

[177]

Dávid Zibriczky. 2016. A combination of simple models by forward predictor selection for job recommendation. In Recommender Systems Challenge (RecSys Challenge’16). Association for Computing Machinery, New York, NY, USA, Article 9, 4 pages. DOI:

Digital Library

Cited By

Alfaifi Y(2024)Recommender Systems Applications: Data Sources, Features, and ChallengesInformation10.3390/info1510066015:10(660)Online publication date: 21-Oct-2024
https://doi.org/10.3390/info15100660
Shin D(2024)Enhancing User Experience in Information Security E-Recruiting PlatformsThe Journal of Korean Institute of Information Technology10.14801/jkiit.2024.22.9.18522:9(185-196)Online publication date: 30-Sep-2024
https://doi.org/10.14801/jkiit.2024.22.9.185
Mashayekhi YKang BLijffijt JDe Bie T(2024)Scalable Job Recommendation With Lower Congestion Using Optimal TransportIEEE Access10.1109/ACCESS.2024.339022912(55491-55505)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3390229
Show More Cited By

Index Terms

A Challenge-based Survey of E-recruitment Recommendation Systems
1. General and reference
  1. Document types
    1. Surveys and overviews
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 10

October 2024

954 pages

EISSN:1557-7341

DOI:10.1145/3613652

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
,
Michela Milano
University of Bologna, Italy

Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2024

Online AM: 18 April 2024

Accepted: 13 April 2024

Revised: 23 February 2024

Received: 05 August 2022

Published in CSUR Volume 56, Issue 10

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European Research Council under the European Union’s Seventh Framework Programme
European Union’s Horizon 2020 research and innovation programme
Special Research Fund (BOF) of Ghent University
Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen

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Alfaifi Y(2024)Recommender Systems Applications: Data Sources, Features, and ChallengesInformation10.3390/info1510066015:10(660)Online publication date: 21-Oct-2024
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Shin D(2024)Enhancing User Experience in Information Security E-Recruiting PlatformsThe Journal of Korean Institute of Information Technology10.14801/jkiit.2024.22.9.18522:9(185-196)Online publication date: 30-Sep-2024
https://doi.org/10.14801/jkiit.2024.22.9.185
Mashayekhi YKang BLijffijt JDe Bie T(2024)Scalable Job Recommendation With Lower Congestion Using Optimal TransportIEEE Access10.1109/ACCESS.2024.339022912(55491-55505)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3390229
Yuehgoh FDjebali STravers N(2024)Leveraging recommendations using a multiplex graph databaseInternational Journal of Web Information Systems10.1108/IJWIS-05-2024-013720:5(537-582)Online publication date: 25-Oct-2024
https://doi.org/10.1108/IJWIS-05-2024-0137
Akkasi A(2024)Job description parsing with explainable transformer based ensemble models to extract the technical and non-technical skillsNatural Language Processing Journal10.1016/j.nlp.2024.1001029(100102)Online publication date: Dec-2024
https://doi.org/10.1016/j.nlp.2024.100102
Saito YSugiyama K(2024)Leveraging multiple behaviors and explicit preferences for job recommendationExpert Systems with Applications10.1016/j.eswa.2024.125149258(125149)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.125149

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