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The Sequence Recommendation Algorithm based on GRU and Attention

Bo He, Kaiwei Zhu, Qingyang Lai
Vol. 5, No. 6, pp. 8-15, 2023 DOI: 10.54691/sjt.v5i6.5155 June 20, 2023
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Abstract.In recent years, with the booming development of social networks and e-commerce, users have increasingly convenient access to information, while a large amount of data continues to emerge, leading to more and more serious information overload. To alleviate the problem of information overload, recommendation systems have emerged to assist users in sifting through massive amounts of information to find content that meets their needs. Sequential recommendation, as a form of recommendation system, mainly analyzes the interaction behavior between users and items, models user characteristics, and then uses various methods to capture users' long-term and short-term preferences to recommend items of interest to users. Based on the perspective of user preference change over time, this paper provides an in-depth analysis of the current research progress and methods of user behavior sequence recommendation. At the same time, this paper proposes corresponding solution strategies for the problems of cold start, sparse matrix and noise interference faced by traditional recommendation systems. Finally, we will discuss the challenges and future research directions of recommendation systems to provide the theoretical basis for further improvement of recommendation systems.
Keywords. Sequential Recommendation; Long-term Preference; Short-term Preference; Cold Start.
APA Citation Bo He, Kaiwei Zhu, Qingyang Lai (2023). The Sequence Recommendation Algorithm based on GRU and Attention. Scientific Journal of Technology, 5(6), 8-15. https://doi.org/10.54691/sjt.v5i6.5155

References

  1. Wang S, Hu L, Wang Y, et al. Sequential recommender systems: challenges, progress and prospects [J]. arXiv preprint arXiv:2001.04830, 2019.
  2. Sarwar B, Karypis G, Konstan J, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th international conference on World Wide Web. 2001: 285-295.
  3. Donkers T, Loepp B, Ziegler J. Sequential user-based recurrent neural network recommendations [C]//Proceedings of the eleventh ACM conference on recommender systems. 2017: 152-160.
  4. Li Z, Zhao H, Liu Q, et al. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors[C]//Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018: 1734-1743.
  5. Hidasi B, Karatzoglou A, Baltrunas L, et al. Session-based recommendations with recurrent neural networks[J]. arXiv preprint arXiv:1511.06939, 2015.
  6. Song Y, Lee J G. Augmenting recurrent neural networks with high-order user-contextual preference for session-based recommendation[J]. arXiv preprint arXiv:1805.02983, 2018.
  7. Kumar V, Khattar D, Gupta S, et al. Deep Neural Architecture for News Recommendation[C]//CLEF (Working Notes). 2017.
  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. 2010: 811-820.
  9. Hosseinzadeh Aghdam M, Hariri N, Mobasher B, et al. Adapting recommendations to contextual changes using hierarchical hidden markov models[C]//Proceedings of the 9th ACM Conference on Recommender Systems. 2015: 241-244.
  10. He R, Fang C, Wang Z, et al. Vista: A visually, socially, and temporally-aware model for artistic recommendation[C]//Proceedings of the 10th ACM conference on recommender systems. 2016: 309-316.
  11. Tang J, Wang K. Personalized top-n sequential recommendation via convolutional sequence embedding [C]//Proceedings of the eleventh ACM international conference on web search and data mining. 2018: 565-573.
  12. Yuan F, He X, Jiang H, et al. Future data helps training: Modeling future contexts for session-based recommendation[C]//Proceedings of The Web Conference 2020. 2020: 303-313.
  13. Liu Q, Zeng Y, Mokhosi R, et al. STAMP: short-term attention/memory priority model for session-based recommendation[C]//Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018: 1831-1839.
  14. Ying H, Zhuang F, Zhang F, et al. Sequential recommender system based on hierarchical attention network[C]//IJCAI International Joint Conference on Artificial Intelligence. 2018.
  15. FENG S S,LI X T,ZENG Y F,et al. Personalized ranking metric embedding for next new POI recommendation. Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI'15). AAAI Press,2015:2069-2075.