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View all- Leng YYu L(2021)Hierarchical Context-Aware Recurrent Network for Session-Based RecommendationIEEE Access10.1109/ACCESS.2021.30698469(51618-51630)Online publication date: 2021
Session-based Recommendation with Context-Aware Attention Network
Pages 141 - 146
Abstract
Session-based recommendation aims to generate recommendation results based on user's anonymous session. Previous studies model the session as a sequence and use Recursive Neural Network (RNN) to represent user behavior for recommendations. Although achieved promising result, previous studies ignore the relationship between session's items and external context of session, which fails in revealing the intrinsic relation between them. To tackle the problem mentioned above, we propose a novel method, i.e., Session-based Recommendation with Context-Aware Attention Network, SR-CAAN, which enhances the ability of modeling the user preference by combining sequence prediction with session external context aware method. In the proposed method, we incorporate external knowledge with Knowledge Graph (KG) to obtain the external context of session by using attention mechanism. Each session is presented as a composition of the external context of session and user's long-short term interest is obtained by Recurrent Neural Networks (RNNs). Experiments conducted on real word datasets demonstrate that SR-CAAN outperform the state-of-the-art significantly.
References
[1]
Joseph Konstan Badrul Sarwar, George Karypisand John Riedl.2001. Item-Based Collaborative Filtering Recommendation Algorithms. ACM1-58113-348-0/01/000 (2001).
[2]
Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76--80.
[3]
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web-WWW'10. ACM Press, New York, New York, USA, 811--820.
[4]
Andrew Zimdars, David Maxwell Chickering, and Christopher Meek. 2001. Using Temporal Datafor Making Recommendations. UAI 2001: 580--588
[5]
Guy Shani, Ronen I. Brafman, and David Heckerman. 2005. An MDP-based Recommender System. Journal of Machine Learning Research 6,1(2005), 1265--1295.
[6]
Guy Shani, Ronen I. Brafman, and David Heckerman. 2013. An MDP-based Recommender System. CoRR abs/1301.0600 (2013).
[7]
O.IrsoyandC. Cardie. 2014. Deep recursive neural networks for compositionality in language. In International Conferenceon Neural Information Processing Systems. 2096--2104.
[8]
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. Computer Science (2016).
[9]
Yong Kiam Tan, Xinxing Xu, and Yong Liu.2016. Improved Recurrent Neural Networks for Session-based Recommendations. DLRS,17--22 (2016).
[10]
Li Jing, Pengjie Ren, Zhumin Chen, Zhaochun Ren, and Jun Ma. 2017. Neural Attentive Session-based Recommendation. CIKM 2017: 1419--1428.
[11]
Trinh Xuan Tuan and Minh Phuong Tu.2017. 3D Convolutional Networks for Session-based Recommendation with Content Features. RecSys 2017: 138--146.
[12]
Antoine Bordes, Nicolas Usunier, Alberto Garcíadurán, Jason Weston, and Oksana Yakhnenko.2013. Translating Embeddings for Modeling Multi-Relational. NIPS 2013: 2787--2795.
[13]
Zhen Wang, Jianwen Zhang, Jianlin Feng, Zheng Chen. 2014. Knowledge Graph Embedding by Translating on Hyperplanes. AAAI 2014: 1112--1119.
[14]
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. AAAI 2015: 2181--2187.
[15]
Hongwei Wang, Fuzheng Zhang, Xie Xing, and Minyi Guo. 2018. DKN: Deep Knowledge-Aware Network for News Recommendation. (2018).
[16]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. CoRR abs/1706.03762(2017).
[17]
Zeno Gantner Steffen Rendle, Christoph Freudenthalerand Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In InUAI. 452--461.
[18]
F.Maxwell Harperand Joseph A.Konstan.2015. The MovieLens Datasets: History and Context. TiiS 5(4): 19:1--19:19 (2016).
[19]
Xu Han, Shulin Cao, LvXin, Yankai Lin, Zhiyuan Liu, Maosong Sun, and Juanzi Li.2018. OpenKE: An Open Toolkit for Knowledge Embedding. EMNLP 2018: 139--144
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Published In
December 2019
601 pages
ISBN:9781450376631
DOI:10.1145/3377170
Copyright © 2019 ACM.
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In-Cooperation
- Shanghai Jiao Tong University: Shanghai Jiao Tong University
- The Hong Kong Polytechnic: The Hong Kong Polytechnic University
- University of Malaya: University of Malaya
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 March 2020
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- Refereed limited
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- the National Key R&D Program of China
- the National Natural Science Foundation of China
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ICIT 2019
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View all- Leng YYu L(2021)Hierarchical Context-Aware Recurrent Network for Session-Based RecommendationIEEE Access10.1109/ACCESS.2021.30698469(51618-51630)Online publication date: 2021
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