Graph Neural Network Integrating Hot Spots and Long and Short-Term Interests for Course Recommendation

doi:10.3778/j.issn.1673-9418.2305096

Abstract

Abstract: In recent years, massive online open courses (MOOCs) platforms provide users with a wealth of learning resources. Nevertheless, information overload remains a pressing concern, necessitating the development of effective personalized course recommendation methods. The existing course recommendation methods disregard the temporal relationship among courses and are unable to capture long-distance dependencies between them. Simultaneously, personalized course recommendation models designed for interactive sequence modeling are confronted with two key issues: how to extract users’ learning interest representation effectively and how to solve cold-start. Based on this, a graph neural network course recommendation model (GHLS4CR) is proposed, which integrates hot spots and long and short-term interests. This model designs two session graph conversion methods, acyclic timing graph and acyclic shortcut graph, to alleviate the problems of temporal information loss and inability to capture long-distance dependencies in existing methods. This model represents users’ long-term and short-term interests at the graph level, and integrates them with popular course information to achieve personalized recommendations while alleviating cold-start issue. A large number of experiments conducted on the XuetangX public dataset MOOCCourse show that GHLS4CR outperforms mainstream recommendation models such as FISSA and LESSR in the field of personalized course recommendation. Compared with the second best LESSR model, Recall@5 is improved by 13.28%, and MRR@5 is improved by 15.50%.

Key words: course recommendation, session-based recommendation, graph neural networks, long and short-term interests, cold-start

摘要： 近年来大规模在线开放课程（MOOCs）平台为用户提供了海量的学习资源，亟需一种有效的个性化课程推荐方法帮助用户解决信息过载问题。现有的课程推荐方法忽略了课程间的时序性且无法较好地捕获课程间的长距离依赖关系，同时面临用户学习兴趣表示和冷启动两个关键问题。基于此，提出一种融合热点与长短期兴趣的图神经网络课程推荐模型（GHLS4CR）。该模型设计无环时序图和无环快捷图两种会话图构建方法来缓解现有方法存在的时序信息丢失和不善于捕获长距离依赖的问题；将用户长短期兴趣进行图级表示，并与热门课程信息进行融合实现个性化推荐，同时缓解冷启动问题。通过在学堂在线（XuetangX）公开数据集MOOCCourse上的大量实验表明，GHLS4CR在个性化课程推荐领域优于FISSA和LESSR等主流推荐模型。与次好的LESSR模型相比，Recall@5提高了13.28%，MRR@5提高了15.50%。

LIU Yuan, DONG Yongquan, CHEN Cheng, JIA Rui, YIN Chan. Graph Neural Network Integrating Hot Spots and Long and Short-Term Interests for Course Recommendation[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(6): 1600-1612.

刘源, 董永权, 陈成, 贾瑞, 印婵. 融合热点与长短期兴趣的图神经网络课程推荐模型[J]. 计算机科学与探索, 2024, 18(6): 1600-1612.

References

[1] AHN J, HONG T. Collaborative filtering for recommender systems: a scalability perspective[J]. International Journal of Electronic Business, 2004, 2(1): 77.
[2] RENDLE S, GANTNER Z, FREUDENTHALER C, et al. Fast context-aware recommendations with factorization machines[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, Jul 24-28, 2011. New York: ACM, 2011: 635-644.
[3] QUADRANA M, KARATZOGLOU A, HIDASI B, et al. Personalizing session-based recommendations with hierarchical recurrent neural networks[C]//Proceedings of the 11th ACM Conference on Recommender Systems, Como, Aug 27-31, 2017. New York: ACM, 2017: 130-137.
[4] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, Dec 4-9, 2017: 5998-6008.
[5] ZHANG M, WU S, GAO M, et al. Personalized graph neural networks with attention mechanism for session-aware recom-mendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(8): 3946-3957.
[6] YING H, ZHUANG F, ZHANG F, et al. Sequential recommender system based on hierarchical attention networks[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Jul 13-19, 2018: 3926-3932.
[7] WU S, TANG Y, ZHU Y, et al. Session-based recommendation with graph neural networks[C]//Proceedings of the 2019 AAAI Conference on Artificial Intelligence. Menlo Park: AAAI, 2019: 346-353.
[8] RENDLE S, FREUDENTHALER C, SCHMIDT-THIEME L. Factorizing personalized Markov chains for next-basket recommendation[C]//Proceedings of the 19th International Conference on World Wide Web, Raleigh, Apr 26-30, 2010. New York: ACM, 2010: 811-820.
[9] KANG W C, MCAULEY J. Self-attentive sequential recom-mendation[C]//Proceedings of the 2018 IEEE International Conference on Data Mining, Sentosa, Nov 17-20, 2018. Piscataway: IEEE, 2018: 197-206.
[10] LIN J, PAN W, MING Z. FISSA: fusing item similarity models with self-attention networks for sequential recommendation[C]//Proceedings of the 14th ACM Conference on Recommender Systems, Sep 22-26, 2020. New York: ACM, 2020: 130-139.
[11] GORI M, MONFARDINI G, SCARSELLI F. A new model for learning in graph domains[C]//Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, Montreal, Jul 31-Aug 4, 2005. Piscataway: IEEE, 2005: 729-734.
[12] 歹杰, 李青山, 褚华, 等. 突破智慧教育: 基于图学习的课程推荐系统[J]. 软件学报, 2022, 33(10): 3656-3672.
DAI J, LI Q S, CHU H, et al. Breakthrough in smart education: course recommendation system based on graph learning[J]. Journal of Software, 2022, 33(10): 3656-3672.
[13] ZHANG Y, YUAN M, ZHAO C, et al. Integrating label pro-pagation with graph convolutional networks for recommenda-tion[J]. Neural Computing and Applications, 2022, 34(10): 8211-8225.
[14] CHEN T, WONG R C W. Handling information loss of graph neural networks for session-based recommendation[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 24, 2020. New York: ACM, 2020: 1172-1180.
[15] SHU J, SHEN X, LIU H, et al. A content-based recommenda-tion algorithm for learning resources[J]. Multimedia Systems, 2018, 24(2): 163-173.
[16] CHATTI M A, DAKOVA S, THUS H, et al. Tag-based colla-borative filtering recommendation in personal learning environments[J]. IEEE Transactions on Learning Technologies, 2013, 6(4): 337-349.
[17] JING X, TANG J. Guess you like: course recommendation in MOOCs[C]//Proceedings of the 2017 International Conference on Web Intelligence, Leipzig, Aug 23-26, 2017. New York: ACM, 2017: 783-789.
[18] CHANG P C, LIN C H, CHEN M H. A hybrid course recom-mendation system by integrating collaborative filtering and artificial immune systems[J]. Algorithms, 2016, 9(3): 47.
[19] ZHANG J, HAO B, CHEN B, et al. Hierarchical reinforcement learning for course recommendation in MOOCs[C]//Proceedings of the 2019 AAAI Conference on Artificial Intel-ligence. Menlo Park: AAAI, 2019: 435-442.
[20] LIN Y, FENG S, LIN F, et al. Adaptive course recommendation in MOOCs[J]. Knowledge-Based Systems, 2021, 224: 107085.
[21] ZHANG M, LIU S, WANG Y. STR-SA: session-based thread recommendation for online course forum with self-attention[C]//Proceedings of the 2020 IEEE Global Engineering Edu-cation Conference, Porto, Apr 1-30, 2020. Piscataway: IEEE, 2020: 374-381.
[22] LIU H, XU Z, ZHANG Q, et al. Integrating users’ long-and short-term preferences for session-based recommendation[C]//Proceedings of the 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design, Hangzhou, May 4-6, 2022. Piscataway: IEEE, 2022: 611-616.
[23] QIU R, LI J, HUANG Z, et al. Rethinking the item order in session-based recommendation with graph neural networks[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, Nov 3-7, 2019. New York: ACM, 2019: 579-588.
[24] LI Q, HAN Z, WU X M. Deeper insights into graph convolutional networks for semi-supervised learning[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, Feb 2-7, 2018. Menlo Park: AAAI, 2018: 3538-3545.
[25] HAMILTON W L, YING Z, LESKOVEC J. Inductive repre-sentation learning on large graphs[C]//Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, Dec 4-9, 2017: 1024-1034.
[26] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Jul 21-26, 2017. Washington: IEEE Computer Society, 2017: 2261-2269.
[27] CHENG H T, KOC L, HARMSEN J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. New York: ACM, 2016: 7-10.
[28] YU J, LUO G, XIAO T, et al. MOOCCube: a large-scale data repository for NLP applications in MOOCs[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 5-20, 2020. Stroudsburg: ACL, 2020: 3135-3142.
[29] LI B, LI J, OU X. Hybrid recommendation algorithm of cross-border e-commerce items based on artificial intelligence and multiview collaborative fusion[J]. Neural Computing and Applications, 2022, 34(9): 6753-6762.
[30] 余文婷, 吴云. 时间感知的双塔型自注意力序列推荐模型[J]. 计算机科学与探索, 2024, 18(1): 175-188.
YU W T, WU Y. Time-aware sequential recommendation model based on dual-tower self-attention[J]. Journal of Fron-tiers of Computer Science and Technology, 2024, 18(1): 175-188.
[31] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]//BILMES J A, NG A Y. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, Montreal, Jun 18-21, 2009: 452-461.
[32] HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks[J]. arXiv:1511.06939, 2015.
[33] TANG J, WANG K. Personalized top-N sequential recommendation via convolutional sequence embedding[C]//Proceedings of the 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, Feb 5-9, 2018. New York: ACM, 2018: 565-573.
[34] ZHANG S, TAY Y, YAO L, et al. Next item recommendation with self-attention[J]. arXiv:1808.06414, 2018.