ABSTRACT
With the revival of neural networks, many studies try to adapt powerful sequential neural models, ıe Recurrent Neural Networks (RNN), to sequential recommendation. RNN-based networks encode historical interaction records into a hidden state vector. Although the state vector is able to encode sequential dependency, it still has limited representation power in capturing complicated user preference. It is difficult to capture fine-grained user preference from the interaction sequence. Furthermore, the latent vector representation is usually hard to understand and explain. To address these issues, in this paper, we propose a novel knowledge enhanced sequential recommender. Our model integrates the RNN-based networks with Key-Value Memory Network (KV-MN). We further incorporate knowledge base (KB) information to enhance the semantic representation of KV-MN. RNN-based models are good at capturing sequential user preference, while knowledge-enhanced KV-MNs are good at capturing attribute-level user preference. By using a hybrid of RNNs and KV-MNs, it is expected to be endowed with both benefits from these two components. The sequential preference representation together with the attribute-level preference representation are combined as the final representation of user preference. With the incorporation of KB information, our model is also highly interpretable. To our knowledge, it is the first time that sequential recommender is integrated with external memories by leveraging large-scale KB information.
- Antoine Bordes, Nicolas Usunier, Alberto Garc'ıa-Durán, Jason Weston, and Oksana Yakhnenko . 2013. Translating Embeddings for Modeling Multi-relational Data NIPS. 2787--2795. Google ScholarDigital Library
- Jingyuan Chen, Hanwang Zhang, Xiangnan He, Wei Liu, Wei Liu, and Tat Seng Chua . 2017. Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention. In SIGIR. 335--344. Google ScholarDigital Library
- Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha . 2018. Sequential Recommendation with User Memory Networks WSDM. Google ScholarDigital Library
- Kyunghyun Cho, Bart Van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio . 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. Computer Science (2014).Google Scholar
- Tim Donkers, Benedikt Loepp, and Jürgen Ziegler . 2017. Sequential User-based Recurrent Neural Network Recommendations ACM RecSys. Google ScholarDigital Library
- John C. Duchi, Elad Hazan, and Yoram Singer . 2011. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. Journal of Machine Learning Research Vol. 12 (2011), 2121--2159. Google ScholarDigital Library
- Junyu Gao, Tianzhu Zhang, and Changsheng Xu . 2017. A Unified Personalized Video Recommendation via Dynamic Recurrent Neural Networks ACM MM. Google ScholarDigital Library
- Google . 2016. Freebase Data Dumps. https://developers.google.com/freebase/data. (2016).Google Scholar
- F. Maxwell Harper and Joseph A. Konstan . 2016. The MovieLens Datasets. TiiS Vol. 5, 4 (2016), 1--19.Google ScholarDigital Library
- Ruining He, Wang Cheng Kang, and Julian Mcauley . 2017 a. Translation-based Recommendation. In ACM RecSys. Google ScholarDigital Library
- Ruining He and Julian Mcauley . 2016. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In WWW. Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat Seng Chua . 2017 b. Neural Collaborative Filtering. In WWW. 173--182. Google ScholarDigital Library
- Jonathan L Herlocker, Joseph A Konstan, Al Borchers, and John Riedl . 1999. An algorithmic framework for performing collaborative filtering SIGIR. 230--237. Google ScholarDigital Library
- Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl . 2000. Explaining collaborative filtering recommendations CSCW. 241--250. Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk . 2015. Session-based Recommendations with Recurrent Neural Networks. Computer Science (2015).Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber . 1997. Long Short-Term Memory. Neural Computation (1997), 1735--1780.Google Scholar
- Yehuda Koren, Robert Bell, and Chris Volinsky . 2009. Matrix Factorization Techniques for Recommender Systems. Computer (2009), 30--37. Google ScholarDigital Library
- Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, and Jun Ma . 2017. Neural Attentive Session-based Recommendation. In CIKM. Google ScholarDigital Library
- Fei Liu and Julien Perez . 2016. Gated End-to-End Memory Networks. In EACL.Google Scholar
- Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, and Liang Wang . 2016. Context-Aware Sequential Recommendation. In ICDM. 1053--1058.Google Scholar
- Alexander Miller, Adam Fisch, Jesse Dodge, Amir Hossein Karimi, Antoine Bordes, and Jason Weston . 2016. Key-Value Memory Networks for Directly Reading Documents EMNLP. 1400--1409.Google Scholar
- Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi . 2017. Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. In RecSys. 130--137. Google ScholarDigital Library
- Massimo Quadrana, Domonkos Tikk, and Domonkos Tikk . 2016. Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations ACM Conference on Recommender Systems. 241--248. Google ScholarDigital Library
- Steffen Rendle . 2012. Factorization Machines with libFM. ACM TIST (2012). Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme . 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback UAI. Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme . 2010. Factorizing personalized Markov chains for next-basket recommendation WWW. Google ScholarDigital Library
- Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl . 2001. Item-based Collaborative Filtering Recommendation Algorithms WWW. Google ScholarDigital Library
- Markus Schedl . 2016. The LFM-1b Dataset for Music Retrieval and Recommendation ICMR. Google ScholarDigital Library
- Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum . 2007. Yago: A Core of Semantic Knowledge. In WWW. 697--706. Google ScholarDigital Library
- Nava Tintarev and Judith Masthoff . 2007. A Survey of Explanations in Recommender Systems. In ICDE. 801--810. Google ScholarDigital Library
- Katrien Verbert, Nikos Manouselis, Xavier Ochoa, Martin Wolpers, Hendrik Drachsler, Ivana Bosnic, and Erik Duval . 2012. Context-Aware Recommender Systems for Learning: A Survey and Future Challenges. TLT (2012), 318--335. Google ScholarDigital Library
- Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng . 2015. Learning Hierarchical Representation Model for NextBasket Recommendation SIGIR. 403--412. Google ScholarDigital Library
- Quan Wang, Zhendong Mao, Bin Wang, and Li Guo . 2017. Knowledge Graph Embedding: A Survey of Approaches and Applications. IEEE TKDE (2017).Google Scholar
- Jason Weston, Sumit Chopra, and Antoine Bordes . 2014. Memory Networks. Eprint Arxiv (2014).Google Scholar
- Chao Yuan Wu, Amr Ahmed, Alex Beutel, How Jing, and How Jing . 2017. Recurrent Recommender Networks. In WSDM. 495--503. Google ScholarDigital Library
- Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan . 2016. A Dynamic Recurrent Model for Next Basket Recommendation SIGIR. 729--732. Google ScholarDigital Library
- Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, and Jiawei Han . 2014. Personalized entity recommendation: a heterogeneous information network approach WSDM. 283--292. Google ScholarDigital Library
- Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, and Nadia Magnenat-Thalmann . 2013. Time-aware point-of-interest recommendation. In SIGIR. 363--372. Google ScholarDigital Library
- Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei Ying Ma . 2016. Collaborative Knowledge Base Embedding for Recommender Systems KDD. Google ScholarDigital Library
- Wayne Xin Zhao, Yanwei Guo, Yulan He, Han Jiang, Yuexin Wu, and Xiaoming Li . 2014. We know what you want to buy: a demographic-based system for product recommendation on microblogs. In KDD.Google Scholar
- Wayne Xin Zhao, Sui Li, Yulan He, Edward Y. Chang, Ji-Rong Wen, and Xiaoming Li . 2016. Connecting Social Media to E-Commerce: Cold-Start Product Recommendation using Microblogging Information. TKDE (2016). Google ScholarDigital Library
Index Terms
- Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks
Recommendations
Memory-Augmented Attention Network for Sequential Recommendation
Web Information Systems Engineering – WISE 2019AbstractAn increased interest in sequential recommendation has been observed in recent years. Many models have been proposed to leverage the sequential user-item interaction data, which includes those based on Markov Chain or recurrent neural networks. ...
Improving Sequential Recommendation with Attribute-Augmented Graph Neural Networks
Advances in Knowledge Discovery and Data MiningAbstractMany practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with. In this paper, we propose an attribute-...
Sequential Recommendation with User Memory Networks
WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data MiningUser preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep ...
Comments