ABSTRACT
Variational autoencoders (VAEs) have been widely applied in recommendations. One reason is that their amortized inferences are beneficial for overcoming the data sparsity. However, in explainable recommendation that generates natural language explanations, they are still rarely explored. Thus, we aim to extend VAE to explainable recommendation. In this task, we find that VAE can generate acceptable explanations for users with few relevant training samples, however, it tends to generate less personalized explanations for users with relatively sufficient samples than autoencoders (AEs). We conjecture that information shared by different users in VAE disturbs the information for a specific user. To deal with this problem, we present PErsonalized VAE (PEVAE) that generates personalized natural language explanations for explainable recommendation. Moreover, we propose two novel mechanisms to aid our model in generating more personalized explanations, including 1) Self-Adaption Fusion (SAF) manipulates the latent space in a self-adaption manner for controlling the influence of shared information. In this way, our model can enjoy the advantage of overcoming the sparsity of data while generating more personalized explanations for a user with relatively sufficient training samples. 2) DEpendence Maximization (DEM) strengthens dependence between recommendations and explanations by maximizing the mutual information. It makes the explanation more specific to the input user-item pair and thus improves the personalization of the generated explanations. Extensive experiments show PEVAE can generate more personalized explanations and further analyses demonstrate the practical effect of our proposed methods.
Supplemental Material
- Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. ArXiv abs/1805.03352 (2018).Google Scholar
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer.Google ScholarDigital Library
- Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, and Samy Bengio. 2016. Generating Sentences from a Continuous Space. In CoNLL.Google Scholar
- Boxing Chen and Colin Cherry. 2014. A Systematic Comparison of Smoothing Techniques for Sentence-Level BLEU. In WMT@ACL.Google Scholar
- H. Chen, Shaoyun Shi, Yunqi Li, and Yongfeng Zhang. 2021. Neural Collaborative Reasoning. Proceedings of the Web Conference 2021 (2021).Google ScholarDigital Library
- Tian Qi Chen, Xuechen Li, Roger B. Grosse, and David Kristjanson Duvenaud. 2018. Isolating Sources of Disentanglement in Variational Autoencoders. In NeurIPS.Google Scholar
- Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and P. Abbeel. 2017. Variational Lossy Autoencoder. ArXiv abs/1611.02731 (2017).Google Scholar
- Xu Chen, Yongfeng Zhang, and Zheng Qin. 2019. Dynamic Explainable Recommendation Based on Neural Attentive Models. In AAAI.Google Scholar
- Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqing Bu, Yining Wang, and Enhong Chen. 2019. Co-Attentive Multi-Task Learning for Explainable Recommendation. In IJCAI.Google Scholar
- Zhiyong Cheng, Ying Ding, Lei Zhu, and M. Kankanhalli. 2018. Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews. Proceedings of the 2018 World Wide Web Conference (2018).Google Scholar
- Michael Denkowski and Alon Lavie. 2014. Meteor Universal: Language Specific Translation Evaluation for Any Target Language. In Proceedings of the EACL 2014 Workshop on Statistical Machine Translation.Google ScholarCross Ref
- Le Fang, Chunyuan Li, Jianfeng Gao, Wen Dong, and Changyou Chen. 2019. Implicit deep latent variable models for text generation. arXiv preprint arXiv:1908.11527 (2019).Google Scholar
- Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, and Gerard de Melo. 2020. Fairness-Aware Explainable Recommendation over Knowledge Graphs. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020).Google ScholarDigital Library
- Deepesh V. Hada, Vijaikumar M., and Shirish K. Shevade. 2021. ReXPlug: Explainable Recommendation Using Plug-and-Play Language Model. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, Canada) (SIGIR '21). Association for Computing Machinery, New York, NY, USA, 81--91. https://doi.org/10.1145/3404835.3462939Google ScholarDigital Library
- Ruining He and Julian McAuley. 2016. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. Proceedings of the 25th International Conference on World Wide Web (2016).Google ScholarDigital Library
- Dan Hendrycks and Kevin Gimpel. 2016. Gaussian Error Linear Units (GELUs). arXiv: Learning (2016).Google Scholar
- R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2019. Learning deep representations by mutual information estimation and maximization. arXiv:1808.06670 [stat.ML]Google Scholar
- Eric Jang, Shixiang Shane Gu, and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. ArXiv abs/1611.01144 (2017).Google Scholar
- Daeryong Kim and Bongwon Suh. 2019. Enhancing VAEs for collaborative filtering: flexible priors & gating mechanisms. Proceedings of the 13th ACM Conference on Recommender Systems (2019).Google ScholarDigital Library
- Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. CoRR abs/1412.6980 (2015).Google Scholar
- Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013).Google Scholar
- Wonsung Lee, Kyungwoo Song, and Il-Chul Moon. 2017. Augmented Variational Autoencoders for Collaborative Filtering with Auxiliary Information. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (2017).Google ScholarDigital Library
- Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A Diversity-Promoting Objective Function for Neural Conversation Models. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, San Diego, California, 110--119. https://doi.org/10. 18653/v1/N16--1014Google ScholarCross Ref
- Lei Li, Yongfeng Zhang, and Li Chen. 2020. Generate Neural Template Explanations for Recommendation. In Proceedings of the 29th ACM International Conference on Information Knowledge Management (Virtual Event, Ireland) (CIKM '20). Association for Computing Machinery, New York, NY, USA, 755--764. https://doi.org/10.1145/3340531.3411992Google ScholarDigital Library
- Lei Li, Yongfeng Zhang, and Li Chen. 2021. EXTRA: Explanation Ranking Datasets for Explainable Recommendation. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (2021).Google ScholarDigital Library
- Lei Li, Yongfeng Zhang, and Li Chen. 2021. Personalized Transformer for Explainable Recommendation. In ACL/IJCNLP.Google Scholar
- Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. 2017. Neural Rating Regression with Abstractive Tips Generation for Recommendation. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017).Google ScholarDigital Library
- Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. Proceedings of the 2018 World Wide Web Conference (2018).Google ScholarDigital Library
- Chin-Yew Lin. 2004. ROUGE: A Package for Automatic Evaluation of Summaries. In ACL 2004.Google Scholar
- Florian Mai, Nikolaos Pappas, Ivan Montero, Noah A Smith, and James Henderson. 2020. Plug and Play Autoencoders for Conditional Text Generation. arXiv preprint arXiv:2010.02983 (2020).Google Scholar
- William C. Mann and Sandra A. Thompson. 1988. Rhetorical Structure Theory: Toward a functional theory of text organization. Text & Talk 8 (1988), 243 -- 281.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 (2013).Google ScholarDigital Library
- Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In EMNLP.Google Scholar
- Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. In NIPS.Google Scholar
- Alec Radford and Karthik Narasimhan. 2018. Improving Language Understanding by Generative Pre-TrainingGoogle Scholar
- Noveen Sachdeva, G. Manco, Ettore Ritacco, and Vikram Pudi. 2019. Sequential Variational Autoencoders for Collaborative Filtering. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (2019).Google ScholarDigital Library
- Xiaoyu Shen, Hui Su, Shuzi Niu, and Vera Demberg. 2018. Improving variational encoder-decoders in dialogue generation. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32.Google ScholarCross Ref
- Shaoyun Shi, H. Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, and Yongfeng Zhang. 2020. Neural Logic Reasoning. Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2020).Google ScholarDigital Library
- Kihyuk Sohn, Xinchen Yan, and Honglak Lee. 2015. Learning Structured Output Representation Using Deep Conditional Generative Models. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS'15). MIT Press, Cambridge, MA, USA, 3483--3491.Google Scholar
- Bin Sun, Shaoxiong Feng, Yiwei Li, Jiamou Liu, and Kan Li. 2021. Generating Relevant and Coherent Dialogue Responses using Self-separated Conditional Variational AutoEncoders. In ACL/IJCNLP.Google Scholar
- Yiyi Tao, Yiling Jia, N. C. Wang, and Hongning Wang. 2019. The FacT: Taming Latent Factor Models for Explainability with Factorization Trees. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019).Google ScholarDigital Library
- Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2018. Multi-Pointer Co-Attention Networks for Recommendation. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018).Google ScholarDigital Library
- Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. ArXiv abs/1706.03762 (2017).Google Scholar
- Nan Wang, Hongning Wang, Yiling Jia, and Yue Yin. 2018. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (2018).Google ScholarDigital Library
- Yizhong Wang, Sujian Li, and Jingfeng Yang. 2018. Toward Fast and Accurate Neural Discourse Segmentation. In EMNLP.Google Scholar
- Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, and Yongfeng Zhang. 2019. Reinforcement Knowledge Graph Reasoning for Explainable Recommendation. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019).Google ScholarDigital Library
- Wenju Xu, Shawn Shahriar Keshmiri, and Guanghui Wang. 2019. Adversarially Approximated Autoencoder for Image Generation and Manipulation. IEEE Transactions on Multimedia 21 (2019), 2387--2396.Google ScholarCross Ref
- Xinchen Yan, Jimei Yang, Kihyuk Sohn, and Honglak Lee. 2016. Attribute2Image: Conditional Image Generation from Visual Attributes. ArXiv abs/1512.00570 (2016).Google Scholar
- Xianwen Yu, Xiaoning Zhang, Yang Cao, and Min Xia. 2019. VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders. In IJCAI.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. Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval (2014).Google ScholarDigital Library
- Yongfeng Zhang, Jiaxin Mao, and Qingyao Ai. 2019. SIGIR 2019 Tutorial on Explainable Recommendation and Search. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR'19). Association for Computing Machinery, New York, NY, USA, 1417--1418. https://doi.org/10.1145/3331184.3331390Google ScholarDigital Library
- Shengjia Zhao, Jiaming Song, and Stefano Ermon. 2017. InfoVAE: Information Maximizing Variational Autoencoders. ArXiv abs/1706.02262 (2017).Google Scholar
- Tiancheng Zhao, Kyusong Lee, and Maxine Eskénazi. 2018. Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation. In ACL.Google Scholar
- Tiancheng Zhao, Ran Zhao, and Maxine Eskenazi. 2017. Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. arXiv preprint arXiv:1703.10960 (2017).Google Scholar
Index Terms
- PEVAE: A Hierarchical VAE for Personalized Explainable Recommendation.
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