demonstration

SIMT: A Semantic Interest Modeling Toolkit

Authors:

Mohamed Amine Chatti,

Ravi Kumar Singh,

Shoeb Ahmed JoarderAuthors Info & Claims

UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

Pages 75 - 78

https://doi.org/10.1145/3450614.3461676

Published: 22 June 2021 Publication History

Abstract

In this paper, we focus on semantic interest modeling and present SIMT as a toolkit that harnesses the semantic information to effectively generate user interest models and compute their similarities. SIMT follows a mixed-method approach that combines unsupervised keyword extraction algorithms, knowledge bases, and word embedding techniques to address the semantic issues in the interest modeling task.

Supplementary Material

MP4 File (UMAP Demo.mp4)

In this demo, we will present the semantic interest modeling toolkit (SIMT) and show how it has been leveraged in the transparent recommendation and interest modeling application (RIMA) to infer interest models of researchers based on their publications extracted from Semantic Scholar and use the inferred interest models to provide personalized recommendations of tweets.

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29.22 MB

References

[1]

F. Abel, C. Hauff, G. Houben, and K. Tao. 2012. Leveraging User Modeling on the Social Web with Linked Data. In ICWE.

[2]

Slobodan Beliga, Ana Meštrović, and Sanda Martinčić-Ipšić. 2015. An overview of graph-based keyword extraction methods and approaches. Journal of information and organizational sciences 39, 1 (2015), 1–20.

[3]

Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic language model. Journal of machine learning research 3, Feb (2003), 1137–1155.

[4]

Christoph Besel, Jörg Schlötterer, and Michael Granitzer. 2016. On the quality of semantic interest profiles for onine social network consumers. ACM Sigapp Applied Computing Review 16 (2016), 5–14.

Digital Library

[5]

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics 5 (2017), 135–146.

[6]

Florian Boudin. 2018. Unsupervised keyphrase extraction with multipartite graphs. arXiv preprint arXiv:1803.08721(2018).

[7]

Adrien Bougouin, Florian Boudin, and Béatrice Daille. 2013. Topicrank: Graph-based topic ranking for keyphrase extraction.

[8]

Ricardo Campos, Vítor Mangaravite, Arian Pasquali, Alípio Mário Jorge, Célia Nunes, and Adam Jatowt. 2018. YAKE! collection-independent automatic keyword extractor. In European Conference on Information Retrieval. Springer, 806–810.

[9]

Corina Florescu and Cornelia Caragea. 2017. Positionrank: An unsupervised approach to keyphrase extraction from scholarly documents. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1105–1115.

[10]

Wael H Gomaa and Aly A Fahmy. 2013. A survey of text similarity approaches. International Journal of Computer Applications 68, 13(2013), 13–18.

[11]

Kazi Saidul Hasan and Vincent Ng. 2014. Automatic keyphrase extraction: A survey of the state of the art. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1262–1273.

[12]

Ludovic Jean-Louis, Michel Gagnon, and Eric Charton. 2013. A knowledge-base oriented approach for automatic keyword extraction. Computación y Sistemas 17, 2 (2013), 187–196.

[13]

Kangmiao Liu, Wei Chen, Jiajun Bu, Chun Chen, and Lijun Zhang. 2007. User modeling for recommendation in blogspace. In 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology-Workshops. IEEE, 79–82.

Digital Library

[14]

Zhiyuan Liu, Wenyi Huang, Yabin Zheng, and Maosong Sun. 2010. Automatic keyphrase extraction via topic decomposition. In Proceedings of the 2010 conference on empirical methods in natural language processing. Association for Computational Linguistics, 366–376.

Digital Library

[15]

Pasquale Lops, Cataldo Musto, Fedelucio Narducci, and Giovanni Semeraro. 2019. Semantics in Adaptive and Personalised Systems. Springer.

[16]

Rishabh Mehrotra, Scott Sanner, Wray Buntine, and Lexing Xie. 2013. Improving lda topic models for microblogs via tweet pooling and automatic labeling. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. ACM, 889–892.

Digital Library

[17]

Matthew Michelson and Sofus A Macskassy. 2010. Discovering users’ topics of interest on twitter: a first look. In Proceedings of the fourth workshop on Analytics for noisy unstructured text data. ACM, 73–80.

Digital Library

[18]

Rada Mihalcea and Andras Csomai. 2007. Wikify! Linking documents to encyclopedic knowledge. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management. 233–242.

Digital Library

[19]

Rada Mihalcea and Paul Tarau. 2004. Textrank: Bringing order into text. In Proceedings of the 2004 conference on empirical methods in natural language processing. 404–411.

[20]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111–3119.

[21]

F. Narducci, C. Musto, G. Semeraro, P. Lops, and M. Degemmis. 2013. Leveraging Encyclopedic Knowledge for Transparent and Serendipitous User Profiles. In UMAP.

[22]

Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank citation ranking: Bringing order to the web.Technical Report. Stanford InfoLab.

[23]

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 1532–1543.

[24]

Guangyuan Piao and John G Breslin. 2016. User modeling on Twitter with WordNet Synsets and DBpedia concepts for personalized recommendations. In proceedings of the 25th ACM international on conference on information and knowledge management. 2057–2060.

Digital Library

[25]

Bayu Yudha Pratama and Riyanarto Sarno. 2015. Personality classification based on Twitter text using Naive Bayes, KNN and SVM. In 2015 International Conference on Data and Software Engineering (ICoDSE). IEEE, 170–174.

[26]

Xiao Pu, Mohamed Amine Chatti, Ulrik Schroeder, 2016. Wiki-lda: A mixed-method approach for effective interest mining on twitter data. In Proceedings Of The 8Th International Conference On Computer Supported Education, Vol 1 (Csedu). Scitepress, 426–433.

Digital Library

[27]

Mandyam Annasamy Raghuram, K Akshay, and K Chandrasekaran. 2016. Efficient user profiling in twitter social network using traditional classifiers. In Intelligent systems technologies and applications. Springer, 399–411.

[28]

Stuart Rose, Dave Engel, Nick Cramer, and Wendy Cowley. 2010. Automatic keyword extraction from individual documents. Text mining: applications and theory 1 (2010), 1–20.

[29]

Nacéra Bennacer Seghouani, Coriane Nana Jipmo, and Gianluca Quercini. 2019. Determining the interests of social media users: two approaches. Information Retrieval Journal 22, 1-2 (2019), 129–158.

[30]

Wei Shen, Jianyong Wang, Ping Luo, and Min Wang. 2013. Linking named entities in tweets with knowledge base via user interest modeling. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 68–76.

Digital Library

[31]

Karen Sparck Jones. 1972. A statistical interpretation of term specificity and its application in retrieval. Journal of documentation 28, 1 (1972), 11–21.

[32]

Mia Stern, Joseph Beck, and Beverly Park Woolf. 1999. Naive Bayes classifiers for user modeling. Center for Knowledge Communication, Computer Science Department, University of Massachusetts (1999).

[33]

Giorgia Di Tommaso, Stefano Faralli, Giovanni Stilo, and Paola Velardi. 2018. Wiki-MID: A Very Large Multi-domain Interests Dataset of Twitter Users with Mappings to Wikipedia. In International Semantic Web Conference.

Digital Library

[34]

Xiaojun Wan and Jianguo Xiao. 2008. Single Document Keyphrase Extraction Using Neighborhood Knowledge. In AAAI, Vol. 8. 855–860.

[35]

I.H. Witten and D. Milne. 2008. An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In Proceeding of AAAI Workshop on Wikipedia and Artificial Intelligence: an Evolving Synergy, AAAI Press, Chicago, USA. 25–30.

[36]

Xiao Yu, Hao Ma, Bo-June Hsu, and Jiawei Han. 2014. On building entity recommender systems using user click log and freebase knowledge. In Proceedings of the 7th ACM international conference on Web search and data mining. 263–272.

Digital Library

Cited By

Guesmi MChatti MKadhim LJoarder SAin Q(2023)Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence EncodersMultimodal Technologies and Interaction10.3390/mti70900917:9(91)Online publication date: 15-Sep-2023
https://doi.org/10.3390/mti7090091
Guesmi MChatti MJoarder SAin QAlatrash RSiepmann CVahidi T(2023)Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender SystemInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226279740:22(7248-7269)Online publication date: 15-Oct-2023
https://doi.org/10.1080/10447318.2023.2262797
Guesmi MChatti MTayyar AAin QJoarder S(2022)Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design ApproachMultimodal Technologies and Interaction10.3390/mti60600426:6(42)Online publication date: 30-May-2022
https://doi.org/10.3390/mti6060042

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Information & Contributors

Information

Published In

cover image ACM Conferences

UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

June 2021

431 pages

ISBN:9781450383677

DOI:10.1145/3450614

Editors:
Judith Masthoff
Utrecht University, The Netherlands and University of Aberdeen, UK
,
Eelco Herder
Radboud University, The Netherlands
,
Nava Tintarev
University of Maastricht, The Netherlands
,
Marko Tkalčič
University of Primorska, Slovenia

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2021

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Demonstration
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UMAP '21

Sponsor:

UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization

June 21 - 25, 2021

Utrecht, Netherlands

Acceptance Rates

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

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Cited By

Guesmi MChatti MKadhim LJoarder SAin Q(2023)Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence EncodersMultimodal Technologies and Interaction10.3390/mti70900917:9(91)Online publication date: 15-Sep-2023
https://doi.org/10.3390/mti7090091
Guesmi MChatti MJoarder SAin QAlatrash RSiepmann CVahidi T(2023)Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender SystemInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226279740:22(7248-7269)Online publication date: 15-Oct-2023
https://doi.org/10.1080/10447318.2023.2262797
Guesmi MChatti MTayyar AAin QJoarder S(2022)Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design ApproachMultimodal Technologies and Interaction10.3390/mti60600426:6(42)Online publication date: 30-May-2022
https://doi.org/10.3390/mti6060042

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