Kyeongpil Kang
ABSTRACT
Deep neural networks improved the accuracy of sequential recommendation approach which takes into account the sequential patterns of user logs, e.g., a purchase history of a user. However, incorporating only the individual's recent logs may not be sufficient in properly reflecting global preferences and trends across all users and items. In response, we propose a self-attentive sequential recommender system with topic modeling-based category embedding as a novel approach to exploit global information in the process of sequential recommendation. Our self-attention module effectively leverages the sequential patterns from the user's recent history. In addition, our novel category embedding approach, which utilizes the information computed by topic modeling, efficiently captures global information that the user generally prefers. Furthermore, to provide diverse recommendations as well as to prevent overfitting, our model also incorporates a vector obtained by random sampling. Experimental studies show that our model outperforms state-of-the-art sequential recommendation models, and that category embedding effectively provides global preference information.
- Rakesh Agrawal, Tomasz Imieli'nski, and Arun Swami. 1993. Mining association rules between sets of items in large databases. In ACM SIGMOD . 207--216.Google Scholar
- R. Agrawal and R. Srikant. 1995. Mining sequential patterns. In ICDE. 3--14.Google Scholar
- David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent Dirichlet allocation. JMLR (2003), 993--1022.Google ScholarDigital Library
- Chenwei Cai, Ruining He, and Julian McAuley. 2017. SPMC: socially-aware personalized markov chains for sparse sequential recommendation. In IJCAI . 1476--1482.Google Scholar
- Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, and Diana Inkpen. 2017. Natural language inference with external knowledge. arXiv:1711.04289 (2017).Google Scholar
- Wen-Yen Chen, Dong Zhang, and Edward Y Chang. 2008. Combinational collaborative filtering for personalized community recommendation. In KDD . 115--123.Google Scholar
- Disheng Dong, Xiaolin Zheng, Ruixun Zhang, and Yan Wang. 2018. Recurrent Collaborative Filtering for Unifying General and Sequential Recommender.. In IJCAI . 3350--3356.Google Scholar
- Songjie Gong. 2010. A collaborative filtering recommendation algorithm based on user clustering and item clustering. Journal of Software (2010), 745--752.Google Scholar
- Ruining He, Wang-Cheng Kang, and Julian McAuley. 2017. Translation-based recommendation. In RecSys. 161--169.Google Scholar
- Ruining He and Julian McAuley. 2016. Fusing similarity models with markov chains for sparse sequential recommendation. In ICDM . 191--200.Google Scholar
- Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, and Tat-Seng Chua. 2018. Outer product-based neural collaborative filtering. In IJCAI . 2227--2233.Google Scholar
- Balázs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In CIKM . 843--852.Google Scholar
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv:1511.06939 (2015).Google Scholar
- Wang-Cheng Kang and Julian McAuley. 2018. Self-Attentive Sequential Recommendation. In ICDM. 197--206.Google Scholar
- Tero Karras, Samuli Laine, and Timo Aila. 2018. A style-based generator architecture for generative adversarial networks. arXiv:1812.04948 (2018).Google Scholar
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980 (2014).Google Scholar
- Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv:1312.6114 (2013).Google Scholar
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. IEEE Computer Society (2009), 30--37.Google ScholarDigital Library
- Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In ICML. 1188--1196.Google Scholar
- Daniel D Lee and H Sebastian Seung. 2001. Algorithms for non-negative matrix factorization. In NIPS. 556--562.Google Scholar
- Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017. Neural attentive session-based recommendation. In CIKM. 1419--1428.Google Scholar
- Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. JMLR (2008), 2579--2605.Google Scholar
- Benjamin M Marlin. 2004. Modeling user rating profiles for collaborative filtering. In NIPS. 627--634.Google Scholar
- Leland McInnes, John Healy, and James Melville. 2018. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 (2018).Google Scholar
- Lei Mei, Pengjie Ren, Zhumin Chen, Liqiang Nie, Jun Ma, and Jian-Yun Nie. 2018. An attentive interaction network for context-aware recommendations. In CIKM . 157--166.Google Scholar
- Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, and Zhi Jin. 2016. Natural Language Inference by Tree-Based Convolution and Heuristic Matching. In ACL . 130--136.Google Scholar
- Shuzi Niu and Rongzhi Zhang. 2017. Collaborative sequence prediction for sequential recommender. In CIKM . 2239--2242.Google Scholar
- Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In ICDM. 502--511.Google Scholar
- Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon, and David MJ Tax. 2017. Interacting attention-gated recurrent networks for recommendation. In CIKM . 1459--1468.Google Scholar
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452--461.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW . 811--820.Google Scholar
- Hanhuai Shan and Arindam Banerjee. 2010. Generalized probabilistic matrix factorizations for collaborative filtering. In ICDM . 1025--1030.Google Scholar
- Shaoyun Shi, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Attention-based Adaptive Model to Unify Warm and Cold Starts Recommendation. In CIKM . 127--136.Google Scholar
- Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In ICWSM . 565--573.Google Scholar
- Yee W Teh, Michael I Jordan, Matthew J Beal, and David M Blei. 2005. Sharing clusters among related groups: Hierarchical Dirichlet processes. In NIPS . 1385--1392.Google Scholar
- Lyle H Ungar and Dean P Foster. 1998. Clustering methods for collaborative filtering. In AAAI workshop on recommendation systems . 114--129.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS. 5998--6008.Google Scholar
- Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In KDD. 1235--1244.Google Scholar
- Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019. Knowledge Graph Convolutional Networks for Recommender Systems. In WWW . 3307--3313.Google Scholar
- Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing. 2017. Recurrent recommender networks. In ICDM. 495--503.Google Scholar
- Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In ICWSM . 153--162.Google Scholar
- Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, and Jian Wu. 2018. Sequential recommender system based on hierarchical attention networks. In IJCAI . 3926--3932.Google Scholar
- Chenyi Zhang, Ke Wang, Hongkun Yu, Jianling Sun, and Ee-Peng Lim. 2014. Latent factor transition for dynamic collaborative filtering. In SDM. 452--460.Google Scholar
- Qi Zhang, Jiawen Wang, Haoran Huang, Xuanjing Huang, and Yeyun Gong. 2017. Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network.. In IJCAI . 3420--3426.Google Scholar
- Shuai Zhang, Yi Tay, Lina Yao, and Aixin Sun. 2018. Next Item Recommendation with Self-Attention. arXiv:1808.06414 (2018).Google Scholar
- Ziwei Zhu, Xia Hu, and James Caverlee. 2018. Fairness-aware tensor-based recommendation. In CIKM. 1153--1162.Google Scholar
Index Terms
- Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling
Recommendations
Improving Accuracy of Recommender System by Item Clustering
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution
AbstractRecommender systems are challenging research problems being exploited to suggest new items or services, such as books, music and movies, and even people, to users based on information about the user profile or the recommended items. To ...
Highlights- A novel temporal multitransition in user preference drift systems was proposed.
A New Approach for Recommender System
ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and SystemsIn today's e-commerce environment, Collaborative Filtering (CF) is a widely used algorithm for recommender system, which is to identify the users who have similar preferences to the target user, and to predict the preference of the target user according ...
Comments