ABSTRACT
Collaborative filtering (CF) is a common recommendation approach that relies on user-item ratings. However, the natural sparsity of user-item rating data can be problematic in many domains and settings, limiting the ability to generate accurate predictions and effective recommendations. Moreover, in some CF approaches latent features are often used to represent users and items, which can lead to a lack of recommendation transparency and explainability. User-generated, customer reviews are now commonplace on many websites, providing users with an opportunity to convey their experiences and opinions of products and services. As such, these reviews have the potential to serve as a useful source of recommendation data, through capturing valuable sentiment information about particular product features. In this paper, we present a novel deep learning recommendation model, which co-learns user and item information from ratings and customer reviews, by optimizing matrix factorization and an attention-based GRU network. Using real-world datasets we show a significant improvement in recommendation performance, compared to a variety of alternatives. Furthermore, the approach is useful when it comes to assigning intuitive meanings to latent features to improve the transparency and explainability of recommender systems.
- Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et almbox.. 2016. Deep speech 2: End-to-end speech recognition in english and mandarin International Conference on Machine Learning. 173--182. Google ScholarDigital Library
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).Google Scholar
- David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research Vol. 3, Jan (2003), 993--1022. Google ScholarDigital Library
- Emmanuel J Candès and Benjamin Recht. 2009. Exact matrix completion via convex optimization. Foundations of Computational mathematics Vol. 9, 6 (2009), 717.Google Scholar
- Li Chen, Guanliang Chen, and Feng Wang. 2015. Recommender systems based on user reviews: the state of the art. User Modeling and User-Adapted Interaction Vol. 25, 2 (2015), 99--154. Google ScholarDigital Library
- Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).Google Scholar
- Robert Desimone and John Duncan. 1995. Neural mechanisms of selective visual attention. Annual review of neuroscience Vol. 18, 1 (1995), 193--222.Google Scholar
- 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) Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 193--202. Google ScholarDigital Library
- Ruihai Dong, Michael P O'Mahony, Markus Schaal, Kevin McCarthy, and Barry Smyth. 2013 a. Sentimental product recommendation. In Proceedings of the 7th ACM conference on Recommender systems. ACM, 411--414. Google ScholarDigital Library
- Ruihai Dong, Michael P O'Mahony, and Barry Smyth. 2014. Further experiments in opinionated product recommendation International Conference on Case-Based Reasoning. Springer, 110--124.Google Scholar
- Ruihai Dong, Markus Schaal, Michael P O'Mahony, Kevin McCarthy, and Barry Smyth. 2013 c. Opinionated product recommendation. In International Conference on Case-Based Reasoning. Springer, 44--58.Google ScholarCross Ref
- Ruihai Dong, Markus Schaal, Michael P O'Mahony, and Barry Smyth. 2013 b. Topic Extraction from Online Reviews for Classification and Recommendation. IJCAI, Vol. Vol. 13. 1310--1316. Google ScholarDigital Library
- Ruihai Dong and Barry Smyth. 2016. Personalized Opinion-Based Recommendation. In International Conference on Case-Based Reasoning. Springer, 93--107.Google Scholar
- Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017. Convolutional sequence to sequence learning. arXiv preprint arXiv:1705.03122 (2017).Google Scholar
- Alex Graves and Jürgen Schmidhuber. 2005. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks Vol. 18, 5--6 (2005), 602--610. Google ScholarDigital Library
- Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. In Advances in Neural Information Processing Systems. 1693--1701. Google ScholarDigital Library
- Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014. A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014).Google Scholar
- Timothy L Keiningham, Bruce Cooil, Lerzan Aksoy, Tor W Andreassen, and Jay Weiner. 2007. The value of different customer satisfaction and loyalty metrics in predicting customer retention, recommendation, and share-of-wallet. Managing Service Quality: An International Journal Vol. 17, 4 (2007), 361--384.Google ScholarCross Ref
- Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. 2016. Convolutional matrix factorization for document context-aware recommendation Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 233--240. Google ScholarDigital Library
- Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014).Google Scholar
- Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 426--434. Google ScholarDigital Library
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer Vol. 42, 8 (2009). Google ScholarDigital Library
- Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky. 2016. Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541 (2016).Google Scholar
- Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing Vol. 7, 1 (2003), 76--80. Google ScholarDigital Library
- Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015).Google Scholar
- Julian McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: understanding rating dimensions with review text Proceedings of the 7th ACM conference on Recommender systems. ACM, 165--172. Google ScholarDigital Library
- Tomávs Mikolov, Martin Karafiát, Lukávs Burget, Jan vCernockỳ, and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In Eleventh Annual Conference of the International Speech Communication Association.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- Andriy Mnih and Ruslan R Salakhutdinov. 2008. Probabilistic matrix factorization. In Advances in neural information processing systems. 1257--1264. Google ScholarDigital Library
- Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 1532--1543.Google ScholarCross Ref
- Alexandrin Popescul, David M Pennock, and Steve Lawrence. 2001. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., 437--444. Google ScholarDigital Library
- Alexander M Rush, Sumit Chopra, and Jason Weston. 2015. A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685 (2015).Google Scholar
- Andrew I Schein, Alexandrin Popescul, Lyle H Ungar, and David M Pennock. 2002. Methods and metrics for cold-start recommendations Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 253--260. Google ScholarDigital Library
- Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016. Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603 (2016).Google Scholar
- Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017. Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction. In Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 297--305. Google ScholarDigital Library
- Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Margaret Mitchell, Jian-Yun Nie, Jianfeng Gao, and Bill Dolan. 2015. A neural network approach to context-sensitive generation of conversational responses. arXiv preprint arXiv:1506.06714 (2015).Google Scholar
- Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks Advances in neural information processing systems. 3104--3112. Google ScholarDigital Library
- Duyu Tang, Bing Qin, and Ting Liu. 2015. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification. EMNLP. 1422--1432.Google Scholar
- Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013. Deep content-based music recommendation. In Advances in neural information processing systems. 2643--2651. Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. arXiv preprint arXiv:1706.03762 (2017).Google Scholar
- Chong Wang and David M Blei. 2011. Collaborative topic modeling for recommending scientific articles Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 448--456. Google ScholarDigital Library
- Yequan Wang, Minlie Huang, Li Zhao, et almbox.. 2016. Attention-based lstm for aspect-level sentiment classification Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 606--615.Google Scholar
- Tsung-Hsien Wen, Milica Gasic, Nikola Mrksic, Pei-Hao Su, David Vandyke, and Steve Young. 2015. Semantically conditioned lstm-based natural language generation for spoken dialogue systems. arXiv preprint arXiv:1508.01745 (2015).Google Scholar
- Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M Rush, Bart van Merriënboer, Armand Joulin, and Tomas Mikolov. 2015. Towards ai-complete question answering: A set of prerequisite toy tasks. arXiv preprint arXiv:1502.05698 (2015).Google Scholar
- Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention International Conference on Machine Learning. 2048--2057. Google ScholarDigital Library
- Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical attention networks for document classification Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1480--1489.Google Scholar
- 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. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, 83--92. Google ScholarDigital Library
Index Terms
- Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews
Recommendations
Cross-representation mediation of user models
Personalization is considered a powerful methodology for improving the effectiveness of information search and decision making. It has led to the dissemination of systems capable of suggesting relevant and personalized information (or items) to the users,...
Improving Cold Start Recommendation by Mapping Feature-Based Preferences to Item Comparisons
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and PersonalizationMany Recommender Systems (RSs) rely on user preference data in the form of ratings or likes for items. Previous research has shown that item comparisons can also be effectively used to model user preferences and build RS. However, users often express ...
Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied ComputingCollaborative filtering recommender systems provide their users with relevant items based on information from other similar users. Popular collaborative filtering approaches such as Pearson correlation coefficient and cosine similarity, compute the ...
Comments