research-article

Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest

Authors:

Ji ZhangAuthors Info & Claims

Journal of Computer Science and Technology, Volume 37, Issue 6

Pages 1444 - 1463

https://doi.org/10.1007/s11390-021-2124-z

Published: 30 November 2022 Publication History

Abstract

Friend recommendation plays a key role in promoting user experience in online social networks (OSNs). However, existing studies usually neglect users' fine-grained interest as well as the evolving feature of interest, which may cause unsuitable recommendation. In particular, some OSNs, such as the online learning community, even have little work on friend recommendation. To this end, we strive to improve friend recommendation with fine-grained evolving interest in this paper. We take the online learning community as an application scenario, which is a special type of OSNs for people to learn courses online. Learning partners can help improve learners' learning effect and improve the attractiveness of platforms. We propose a learning partner recommendation framework based on the evolution of fine-grained learning interest (LPRF-E for short). We extract a sequence of learning interest tags that changes over time. Then, we explore the time feature to predict evolving learning interest. Next, we recommend learning partners by fine-grained interest similarity. We also refine the learning partner recommendation framework with users' social influence (denoted as LPRF-F for differentiation). Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy (i.e., approximate 50% improvements on the precision and the recall) and can recommend learning partners with high quality (e.g., more experienced and helpful).

References

[1]

Sakulwichitsintu S, Colbeck D, Ellis L et al. A peer learning framework for enhancing students' learning experiences in online environments. In Proc. the 18th IEEE International Conference on Advanced Learning Technologies, July 2018, pp.168-169.

[2]

Zewail-Foote M Pivoting an upper-level, project-based biochemistry laboratory class to online learning during COVID-19: Enhancing research skills and using community outreach to engage undergraduate students Journal of Chemical Education 2020 97 9 2727-2732

[3]

Gong JB, Zhao Y, Chen S, et al. Hybrid deep neural networks for friend recommendations in edge computing environment IEEE Access 2020 8 10693-10706

[4]

He C B, Li H C, Fei X et al. A topic community-based method for friend recommendation in large-scale online social networks. Concurrency and Computation: Practice and Experience, 2017, 29(6): Article No. e3924.

[5]

Cheng Z Y, Ding Y, Zhu L et al. Aspect-aware latent factor model: Rating prediction with ratings and reviews. In Proc. the 2018 World Wide Web Conference, April 2018, pp.639-648.

[6]

Qi T, Wu F Z, Wu C H et al. HieRec: Hierarchical user interest modeling for personalized news recommendation. In Proc. the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, August 2021, pp.5446-5456.

[7]

Wang H Z, Liu G F, Liu A et al. DMRAN: A hierarchical fine-grained attention based network for recommendation. In Proc. the 28th International Joint Conferences on Artificial Intelligence Organization, August 2019, pp.3698-3704.

[8]

Huang C L, Jiang W J, Wu J et al. Personalized review recommendation based on users' aspect sentiment. ACM Transactions on Internet Technology, 2020, 20(4): Article No. 42.

[9]

Jiang W J, Chen J, Ding X F et al. Review summary generation in online systems: Frameworks for supervised and unsupervised scenarios. ACM Transactions on the Web, 2021, 15(3): Article No. 13.

[10]

Lima E, Shi W S, Liu X M et al. Integrating multi-level tag recommendation with external knowledge bases for automatic question answering. ACM Transactions on Internet Technology, 2019, 19(3): Article No. 34.

[11]

Shao M M, Jiang W J, Zhang L. FRFP: A friend recommendation method based on fine-grained preference. In Proc. the 7th International Conference on Smart City and Informatization, November 2019, pp.35-48.

[12]

Wu B X, Xiao J, Chen J M. Friend recommendation by user similarity graph based on interest in social tagging systems. In Proc. the 11th International Conference on Intelligent Computing, August 2015, pp.375-386.

[13]

Zhou T C, Ma H, Lyu M R, King I. UserRec: A user recommendation framework in social tagging systems. In Proc. the 24th AAAI Conference on Artificial Intelligence, July 2010, pp.1486-1491.

[14]

Bai T, Zhang Y, Wu B et al. Temporal graph neural networks for social recommendation. In Proc. the 2020 IEEE International Conference on Big Data, December 2020, pp.898-903.

[15]

Fan W Q, Ma Y, Li Q et al. Graph neural networks for social recommendation. In Proc. the 2019 World Wide Web Conference, May 2019, pp.417-426.

[16]

Gong J B. Sequential recommendation through graph neural networks and transformer encoder with degree encoding. Algorithms, 2021, 14(9): Article No. 263.

[17]

Guo L, Tang L, Chen T et al. DA-GCN: A domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. In Proc. the 30th International Joint Conference on Artificial Intelligence, August 2021, pp.2483-2489.

[18]

Tao Y, Wang C, Yao L et al. Item trend learning for sequential recommendation system using gated graph neural network. Neural Computing and Applications, 2021.

[19]

Devlin J, Chang M W, Lee K et al. BERT: Pre-training of deep bidirectional transformers for language understanding. https://arxiv.org/pdf/1810.04805.pdf, Nov. 2022.

[20]

Sun F, Liu J, Wu J et al. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proc. the 28th ACM International Conference on Information and Knowledge Management, November 2019, pp.1441-1450.

[21]

Sarwar B, Karypis G, Konstan J et al. Item-based collaborative filtering recommendation algorithms. In Proc. the 10th International Conference on World Wide Web, May 2001, pp.285-295.

[22]

Koren Y, Bell R, and Volinsky C Matrix factorization techniques for recommender systems Computer 2009 42 8 30-37

[23]

He X N, Liao L Z, Zhang H W et al. Neural collaborative filtering. In Proc. the 26th International Conference on World Wide Web, April 2017, pp.173-182.

[24]

Hochreiter S and Schmidhuber J Long short-term memory Neural Computation 1997 9 8 1735-1780

[25]

Yu F, Liu Q, Wu S et al. A dynamic recurrent model for next basket recommendation. In Proc. the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2016, pp.729-732.

[26]

Lv F Y, Jin T W, Yu C L et al. SDM: Sequential deep matching model for online large-scale recommender system. In Proc. the 28th ACM International Conference on Information and Knowledge Management, November 2019, pp.2635-2643.

[27]

Zhu Y, Li H, Liao Y K et al. What to do next: Modeling user behaviors by time-LSTM. In Proc. the 26th International Joint Conference on Artificial Intelligence, August 2017, pp.3602-3608.

[28]

Yu Z P, Lian J X, Mahmoody A et al. Adaptive user modeling with long and short-term preferences for personalized recommendation. In Proc. the 28th International Joint Conference on Artificial Intelligence, August 2019, pp.4213-4219.

[29]

Zhao W, Wang B Y, Ye J B et al. PLASTIC: Prioritize long and short-term information in top-n recommendation using adversarial training. In Proc. the 27th International Joint Conference on Artificial Intelligence, July 2018, pp.3676-3682.

[30]

Liu Q, Zeng Y F, Mokhosi R et al. STAMP: Short-term attention/memory priority model for session-based recommendation. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2018, pp.1831-1839.

[31]

Lei K, Fu QA, Yang M, et al. Tag recommendation by text classification with attention-based capsule network Neurocomputing 2020 391 65-73

[32]

Mihalcea R, Tarau P. TextRank: Bringing order into texts. In Proc. the 2004 Conference on Empirical Methods in Natural Language Processing, July 2004, pp.404-411.

[33]

Li S, Zhao Z, Hu R F et al. Analogical reasoning on Chinese morphological and semantic relations. In Proc. the 56th Annual Meeting of the Association for Computational Linguistics, July 2018, pp.138-143.

[34]

Nahler G. Pearson correlation coefficient. In Dictionary of Pharmaceutical Medicine, Nahler G (ed.), Springer, 2009, pp.132.

[35]

Nir F, Stuart R. Image segmentation in video sequences: A probabilistic approach. In Proc. the 13th Conference on Uncertainty in Artificial Intelligence, August 1997, pp.175-181.

[36]

Hartigan J A, Wong M A. A K-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 1979, 28(1): 100-108.

[37]

Frey BJ and Dueck D Clustering by passing messages between data points Science 2007 315 5814 972-976

[38]

Page L, Brin S, Motwani R et al. The PageRank citation ranking: Bringing order to the web. Technical Report, Stanford InfoLab, 1998. http://ilpubs.stanford.edu:8-090/422/1/1999-66.pdf, Jul. 2022.

[39]

Huo YF, Chen BL, Tang J, et al. Privacy-preserving point-of-interest recommendation based on geographical and social influence Information Sciences 2021 543 202-218

[40]

Wang J, Che Y, Li D. Research on the relation-ship between students' final grade and online learning behavior in blended learning model: Taking business Ethics and CSR course as an example. In Proc. the 2nd International Conference on Education, Knowledge and Information Management, January 2021, pp.159-166.

[41]

Wang TC and Lee HD Developing a fuzzy TOPSIS approach based on subjective weights and objective weights Expert Systems with Applications 2009 36 5 8980-8985

[42]

Rosenberg A, Hirschberg J. V-Measure: A conditional entropy-based external cluster evaluation measure. In Proc. the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 2007, pp.410-420.

[43]

Vinh NX, Epps J, and Bailey J Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance Journal of Machine Learning Research 2010 11 2837-2854

[44]

Rousseeuw PJ Silhouettes: A graphical aid to the interpretation and validation of cluster analysis Journal of Computational and Applied Mathematics 1987 20 53-65

[45]

Lobo J, Jiménez-Valverde A, and Real R AUC: A misleading measure of the predictive distribution models Global Ecology and Biogeography 2008 17 2 145-151

[46]

Goutte C, Gaussier R. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In Proc. the 27th European Conference on Information Retrieval Research, March 2005, pp.345-359. 25.

[47]

Huang SR, Zhang J, Wang L, et al. Social friend recommendation based on multiple network correlation IEEE Transactions on Multimedia 2016 18 2 287-299

[48]

Qader SA and Abbas AR Dual-stage social friend recommendation system based on user interests Iraqi Journal of Science 2020 61 7 1759-1772

[49]

Yin Y, Feng X. Friend recommendation algorithm based on interest and cognition combined with feedback mechanism. In Proc. the 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, August 2019, pp.1025-1030.

[50]

Usman U B, Umar K. Toward a hybrid technique for friends recommendation system in social Tagging. In Proc. the 3rd International Engineering Conference, September 2019, pp.404-410.

Cited By

El Mourabit IJai Andaloussi SOuchetto OMiyara M(2023)Exploring Collaborative Filtering Algorithms in MOOCs Recommender Systems: A Comprehensive OverviewProceedings of the 6th International Conference on Networking, Intelligent Systems & Security10.1145/3607720.3607742(1-5)Online publication date: 24-May-2023
https://dl.acm.org/doi/10.1145/3607720.3607742
Chen JJiang WWu JLi KLi K(2023)Dynamic Personalized POI Sequence Recommendation with Fine-Grained ContextsACM Transactions on Internet Technology10.1145/358368723:2(1-28)Online publication date: 19-May-2023
https://dl.acm.org/doi/10.1145/3583687

Index Terms

Improving Friend Recommendation for Online Learning with Fine-Grained Evolving Interest
1. Human-centered computing

Index terms have been assigned to the content through auto-classification.

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cover image Journal of Computer Science and Technology

Journal of Computer Science and Technology Volume 37, Issue 6

Dec 2022

265 pages

ISSN:1000-9000

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© Institute of Computing Technology, Chinese Academy of Sciences 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 30 November 2022

Accepted: 30 October 2022

Received: 31 December 2021

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Cited By

El Mourabit IJai Andaloussi SOuchetto OMiyara M(2023)Exploring Collaborative Filtering Algorithms in MOOCs Recommender Systems: A Comprehensive OverviewProceedings of the 6th International Conference on Networking, Intelligent Systems & Security10.1145/3607720.3607742(1-5)Online publication date: 24-May-2023
https://dl.acm.org/doi/10.1145/3607720.3607742
Chen JJiang WWu JLi KLi K(2023)Dynamic Personalized POI Sequence Recommendation with Fine-Grained ContextsACM Transactions on Internet Technology10.1145/358368723:2(1-28)Online publication date: 19-May-2023
https://dl.acm.org/doi/10.1145/3583687

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