Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

ABSTRACT

The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user preferences. However, the heterogeneous nature of this information makes it difficult for recommender systems to leverage in a unified framework to boost the performance. Recently, the rapid development of representation learning techniques provides an approach to this problem. By translating the various information sources into a unified representation space, it becomes possible to integrate heterogeneous information for informed recommendation.

In this work, we propose a Joint Representation Learning (JRL) framework for top-N recommendation. In this framework, each type of information source (review text, product image, numerical rating, etc) is adopted to learn the corresponding user and item representations based on available (deep) representation learning architectures. Representations from different sources are integrated with an extra layer to obtain the joint representations for users and items. In the end, both the per-source and the joint representations are trained as a whole using pair-wise learning to rank for top-N recommendation. We analyze how information propagates among different information sources in a gradient-descent learning paradigm, based on which we further propose an extendable version of the JRL framework (eJRL), which is rigorously extendable to new information sources to avoid model re-training in practice.

By representing users and items into embeddings offline, and using a simple vector multiplication for ranking score calculation online, our framework also has the advantage of fast online prediction compared with other deep learning approaches to recommendation that learn a complex prediction network for online calculation.

References

1. Silvana Aciar, Debbie Zhang, Simeon Simoff, and John Debenham. 2007. Informed recommender: Basing recommendations on consumer product reviews. IEEE Intelligent systems Vol. 22, 3 (2007), 39--47.
2. Amjad Almahairi, Kyle Kastner, Kyunghyun Cho, and Aaron Courville. 2015. Learning distributed representations from reviews for collaborative filtering RecSys. 147--154.
3. Yang Bao, Hui Fang, and Jie Zhang. 2014. TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation. AAAI.
4. Konstantin Bauman, Bing Liu, and Alexander Tuzhilin. 2017. Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews. In KDD. 717--725.
5. Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. PAMI (2013).
6. Robin Burke. 2002. Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction Vol. 12, 4 (2002), 331--370.
7. Robin Burke. 2007. Hybrid web recommender systems. The adaptive web. Springer, 377--408.
8. Li Chen, Guanliang Chen, and Feng Wang. 2015. Recommender systems based on user reviews: the state of the art. User Model. & User-Adapt. Inter. (2015).
9. Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. 2016. Learning to rank features for recommendation over multiple categories SIGIR.
10. Xu Chen, Yongfeng Zhang, Qingyao Ai, Hongteng Xu, Junchi Yan, and Zheng Qin. 2017. Personalized key frame recommendation. In SIGIR. 315--324.
11. Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2016. Fast and accurate deep network learning by exponential linear units (elus). ICLR (2016).
12. Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010. Performance of recommender algorithms on top-n recommendation tasks RecSys.
13. Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J Smola, Jing Jiang, and Chong Wang. 2014. Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars) KDD.
14. Michael D. Ekstrand, John T. Riedl, and Joseph A. Konstan. 2011. Collaborative Filtering Recommender Systems. Foundations and Trends in HCI Vol. 4, 2 (2011).
15. Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. 2015. A multi-view deep learning approach for cross domain user modeling in recommendation systems WWW.
16. Asela Gunawardana and Christopher Meek. 2009. A unified approach to building hybrid recommender systems RecSys. 117--124.
17. Ruining He and Julian McAuley. 2016 a. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW.
18. Ruining He and Julian McAuley. 2016 b. VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback AAAI.
19. Xiangnan He and Tat-Seng Chua. 2017. Neural Factorization Machines for Sparse Predictive Analytics SIGIR.
20. Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In WWW. 173--182.
21. Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, and others. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Sig. Proc. (2012).
22. Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer, Vol. 42, 8 (2009).
23. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks NIPS. 1097--1105.
24. Quoc V Le and Tomas Mikolov. 2014. Distributed Representations of Sentences and Documents. ICML, Vol. Vol. 14. 1188--1196.
25. Sheng Li, Jaya Kawale, and Yun Fu. 2015. Deep collaborative filtering via marginalized denoising auto-encoder CIKM. ACM, 811--820.
26. Guang Ling, Michael R Lyu, and Irwin King. 2014. Ratings meet reviews, a combined approach to recommend Proceedings of the 8th ACM Conference on Recommender systems. ACM, 105--112.
27. Julian McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: understanding rating dimensions with review text RecSys. 165--172.
28. Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015. Image-based recommendations on styles and substitutes SIGIR. ACM.
29. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality Advances in neural information processing systems. 3111--3119.
30. M. J. Pazzani and D. Billsus. 2007. Content-Based Recommendation Systems. The Adaptive Web LNCS (2007), 325--341.
31. Steffen Rendle, C. Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback UAI.
32. Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence Vol. 4 (2009).
33. Shiliang Sun. 2013. A survey of multi-view machine learning. Neural Computing and Applications Vol. 23, 7--8 (2013), 2031--2038.
34. Yunzhi Tan, Min Zhang, Yiqun Liu, and Shaoping Ma. 2016. Rating-boosted latent topics: understanding users and items with ratings and reviews IJCAI.
35. Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013. Deep content-based music recommendation. In NIPS. 2643--2651.
36. Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems KDD. ACM, 1235--1244.
37. Xinxi Wang and Ye Wang. 2014. Improving content-based and hybrid music recommendation using deep learning ACM Multimedia. 627--636.
38. Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems WSDM.
39. Yao Wu and Martin Ester. 2015. Flame: A probabilistic model combining aspect based opinion mining and collaborative filtering WSDM. 199--208.
40. Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016 b. Collaborative Knowledge Base Embedding for Recommender Systems KDD.
41. Wei Zhang, Quan Yuan, Jiawei Han, and Jianyong Wang. 2016 a. Collaborative Multi-Level Embedding Learning from Reviews for Rating Prediction IJCAI.
42. Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014. Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis. SIGIR (2014), 83--92.
43. Kaiqi Zhao, Gao Cong, Quan Yuan, and Kenny Q Zhu. 2015. Sar: a sentiment-aspect-region model for user preference analysis in geo-tagged reviews ICDE.
44. Wayne Xin Zhao, Jinpeng Wang, Yulan He, Ji-Rong Wen, Edward Y Chang, and Xiaoming Li. 2016. Mining Product Adopter Information from Online Reviews for Improving Product Recommendation. TKDD, Vol. 10, 3 (2016), 29.
45. Lei Zheng, Vahid Noroozi, and Philip S. Yu. 2017. Joint deep modeling of users and items using reviews for recommendation WSDM.
46. Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. 2016. A Neural Autoregressive Approach to Collaborative Filtering. NIPS (2016).

Index Terms

• Computing methodologies

  • Machine learning

    • Machine learning approaches

      • Learning latent representations

• Human-centered computing

  • Collaborative and social computing

    • Collaborative and social computing theory, concepts and paradigms

      • Collaborative filtering

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Recommender systems