ABSTRACT
In many recommender systems, users express item opinions through two kinds of behaviors: giving preferences and writing detailed reviews. As both kinds of behaviors reflect users’ assessment of items, review enhanced recommender systems leverage these two kinds of user behaviors to boost recommendation performance. On the one hand, researchers proposed to better model the user and item embeddings with additional review information for enhancing preference prediction accuracy. On the other hand, some recent works focused on automatically generating item reviews for recommendation explanations with related user and item embeddings. We argue that, while the task of preference prediction with the accuracy goal is well recognized in the community, the task of generating reviews for explainable recommendation is also important to gain user trust and increase conversion rate. Some preliminary attempts have considered jointly modeling these two tasks, with the user and item embeddings are shared. These studies empirically showed that these two tasks are correlated, and jointly modeling them would benefit the performance of both tasks.
In this paper, we make a further study of unifying these two tasks for explainable recommendation. Instead of simply correlating these two tasks with shared user and item embeddings, we argue that these two tasks are presented in dual forms. In other words, the input of the primal preference prediction task is exactly the output of the dual review generation task , with and denote the preference value space and review space. Therefore, we could explicitly model the probabilistic correlation between these two dual tasks with . We design a unified dual framework of how to inject the probabilistic duality of the two tasks in the training stage. Furthermore, as the detailed preference and review information are not available for each user-item pair in the test stage, we propose a transfer learning based model for preference prediction and review generation. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model for both user preference prediction and review generation.
- Amjad Almahairi, Kyle Kastner, Kyunghyun Cho, and Aaron Courville. 2015. Learning distributed representations from reviews for collaborative filtering. In RecSys. 147–154.Google Scholar
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In ICLR.Google Scholar
- Rose Catherine and William Cohen. 2017. Transnets: Learning to transform for recommendation. In RecSys. 288–296.Google ScholarDigital Library
- Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural attentional rating regression with review-level explanations. In WWW. 1583–1592.Google Scholar
- Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2019. Social Attentional Memory Network: Modeling Aspect- and Friend-Level Differences in Recommendation. In WSDM. 177–185.Google Scholar
- Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2019. Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation. In SIGIR. 765–774.Google Scholar
- Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqin Bu, Yining Wang, and Enhong Chen. 2019. Co-Attentive Multi-Task Learning for Explainable Recommendation. In IJCAI. 1237–2143.Google Scholar
- Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma. 2016. Dual learning for machine translation. In NIPS. 820–828.Google Scholar
- Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. 2015. Trirank: Review-aware explainable recommendation by modeling aspects. In CIKM. 1661–1670.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173–182.Google Scholar
- Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In SIGKDD. 426–434.Google Scholar
- Wu Le, Chen Lei, Hong Richang, Fu Yanjie, Xie Xing, and Wang Meng. 2019. A Hierarchical Attention Model for Social Contextual Image Recommendation. TKDE (2019).Google Scholar
- Piji Li, Zihao Wang, Lidong Bing, and Wai Lam. 2019. Persona-Aware Tips Generation. In WWW. 1006–1016.Google Scholar
- Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. 2017. Neural rating regression with abstractive tips generation for recommendation. In SIGIR. 345–354.Google Scholar
- Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In ACL. 74–81.Google Scholar
- Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten de Rijke. 2019. Explainable fashion recommendation with joint outfit matching and comment generation. TKDE (2019).Google Scholar
- Guang Ling, Michael R Lyu, and Irwin King. 2014. Ratings meet reviews, a combined approach to recommend. In RecSys. 105–112.Google Scholar
- Yichao Lu, Ruihai Dong, and Barry Smyth. 2018. Coevolutionary recommendation model: Mutual learning between ratings and reviews. In WWW. 773–782.Google Scholar
- Yichao Lu, Ruihai Dong, and Barry Smyth. 2018. Why I like it: multi-task learning for recommendation and explanation. In RecSys. 4–12.Google Scholar
- Julian McAuley and Alex Yang. 2016. Addressing complex and subjective product-related queries with customer reviews. In WWW. 625–635.Google Scholar
- Andriy Mnih and Ruslan R Salakhutdinov. 2008. Probabilistic matrix factorization. In NIPS. 1257–1264.Google Scholar
- Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In ACL. 311–318.Google Scholar
- Steffen Rendle. 2010. Factorization machines. In ICDM. 995–1000.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
- 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 RecSys. 297–305.Google Scholar
- Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017. Representation learning of users and items for review rating prediction using attention-based convolutional neural network. In SDM Workshop.Google Scholar
- Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. 2016. Building end-to-end dialogue systems using generative hierarchical neural network models. In AAAI. 3776–3784.Google Scholar
- Martin Sundermeyer, Ralf Schlüter, and Hermann Ney. 2012. LSTM Neural Networks for Language Modeling. In INTERSPEECH. 194–197.Google Scholar
- Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In NIPS. 3104–3112.Google Scholar
- Quoc-Thuan Truong and Hady W.Lauw. 2019. Multimodal Review Generation for Recommender Systems. In WWW. 1864–1874.Google Scholar
- Mengting Wan and Julian McAuley. 2016. Modeling ambiguity, subjectivity, and diverging viewpoints in opinion question answering systems. In ICDM. 489–498.Google Scholar
- Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, and Zhongyuan Wang. 2019. Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. In KDD. 968–977.Google Scholar
- Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2019. Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation. In WWW. 2000–2010.Google Scholar
- Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, and Xing Xie. 2018. A Reinforcement Learning Framework for Explainable Recommendation. In ICDM. 587–596.Google Scholar
- Le Wu, Yong Ge, Qi Liu, Enhong Chen, Richang Hong, Junping Du, and Meng Wang. 2017. Modeling the evolution of users’ preferences and social links in social networking services. TKDE 29, 6 (2017), 1240–1253.Google ScholarDigital Library
- Le Wu, Yong Ge, Qi Liu, Enhong Chen, Bai Long, and Zhenya Huang. 2016. Modeling users’ preferences and social links in social networking services: a joint-evolving perspective. In AAAI. 279–286.Google Scholar
- Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, and Meng Wang. 2019. A Neural Influence Diffusion Model for Social Recommendation. In SIGIR. 235–244.Google Scholar
- Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, and Tie-Yan Liu. 2017. Dual supervised learning. In ICML. 3789–3798.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. In ICML. 2048–2057.Google Scholar
- Hongyu Zang and Xiaojun Wan. 2017. Towards automatic generation of product reviews from aspect-sentiment scores. In ICNLG. 168–177.Google Scholar
- Kun Zhang, Guangyi Lv, Le Wu, Enhong Chen, Qi Liu, Han Wu, Xing Xie, and Fangzhao Wu. 2019. Multilevel Image-Enhanced Sentence Representation Net for Natural Language Inference. Trans. SMC: System (2019).Google Scholar
- Tao Zhang, Jin Zhang, Chengfu Huo, and Ren Weijun. 2019. Automatic Generation of Pattern-controlled Product Description in E-commerce.. In WWW. 2355–2365.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 SIGIR. 83–92.Google Scholar
- Lujun Zhao, Kaisong Song, Changlong Sun, Qi Zhang, Xuanjing Huang, and Xiaozhong Liu. 2019. Review Response Generation in E-Commerce Platforms with External Product Information.. In WWW. 2425–2435.Google Scholar
- Lei Zheng, Vahid Noroozi, and Philip S Yu. 2017. Joint deep modeling of users and items using reviews for recommendation. In WSDM. 425–434.Google Scholar
- Ming Zhou, Mirella Lapata, Furu Wei, Li Dong, Shaohan Huang, and Ke Xu. 2017. Learning to Generate Product Reviews from Attributes. In EACL. 623–632.Google Scholar
- Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In ICCV. 2223–2232.Google Scholar
Index Terms
- Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation
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