{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T21:01:30Z","timestamp":1773176490893,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,6]]},"DOI":"10.1145\/3477495.3532039","type":"proceedings-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T15:12:08Z","timestamp":1657206728000},"page":"692-702","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["PEVAE"],"prefix":"10.1145","author":[{"given":"Zefeng","family":"Cai","sequence":"first","affiliation":[{"name":"East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zerui","family":"Cai","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. ArXiv abs\/1805.03352","author":"Ai Qingyao","year":"2018","unstructured":"Qingyao Ai , Vahid Azizi , Xu Chen , and Yongfeng Zhang . 2018. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. ArXiv abs\/1805.03352 ( 2018 ). Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. ArXiv abs\/1805.03352 (2018)."},{"key":"e_1_3_2_2_2_1","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop Christopher M.","unstructured":"Christopher M. Bishop . 2006. Pattern Recognition and Machine Learning . Springer . Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Samuel R. Bowman Luke Vilnis Oriol Vinyals Andrew M. Dai Rafal J\u00f3zefowicz and Samy Bengio. 2016. Generating Sentences from a Continuous Space. In CoNLL.  Samuel R. Bowman Luke Vilnis Oriol Vinyals Andrew M. Dai Rafal J\u00f3zefowicz and Samy Bengio. 2016. Generating Sentences from a Continuous Space. In CoNLL.","DOI":"10.18653\/v1\/K16-1002"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Boxing Chen and Colin Cherry. 2014. A Systematic Comparison of Smoothing Techniques for Sentence-Level BLEU. In WMT@ACL.  Boxing Chen and Colin Cherry. 2014. A Systematic Comparison of Smoothing Techniques for Sentence-Level BLEU. In WMT@ACL.","DOI":"10.3115\/v1\/W14-3346"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449973"},{"key":"e_1_3_2_2_6_1","unstructured":"Tian Qi Chen Xuechen Li Roger B. Grosse and David Kristjanson Duvenaud. 2018. Isolating Sources of Disentanglement in Variational Autoencoders. In NeurIPS.  Tian Qi Chen Xuechen Li Roger B. Grosse and David Kristjanson Duvenaud. 2018. Isolating Sources of Disentanglement in Variational Autoencoders. In NeurIPS."},{"key":"e_1_3_2_2_7_1","unstructured":"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).  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)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Xu Chen Yongfeng Zhang and Zheng Qin. 2019. Dynamic Explainable Recommendation Based on Neural Attentive Models. In AAAI.  Xu Chen Yongfeng Zhang and Zheng Qin. 2019. Dynamic Explainable Recommendation Based on Neural Attentive Models. In AAAI.","DOI":"10.1609\/aaai.v33i01.330153"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"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.  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.","DOI":"10.24963\/ijcai.2019\/296"},{"key":"e_1_3_2_2_10_1","volume-title":"Proceedings of the 2018 World Wide Web Conference","author":"Cheng Zhiyong","year":"2018","unstructured":"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 ). 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)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-3348"},{"key":"e_1_3_2_2_12_1","volume-title":"Implicit deep latent variable models for text generation. arXiv preprint arXiv:1908.11527","author":"Fang Le","year":"2019","unstructured":"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 ). 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)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401051"},{"key":"e_1_3_2_2_14_1","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Hada Deepesh V.","unstructured":"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\/3404835.3462939 10.1145\/3404835.3462939 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\/3404835.3462939"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883037"},{"key":"e_1_3_2_2_16_1","volume-title":"Gaussian Error Linear Units (GELUs). arXiv: Learning","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel . 2016. Gaussian Error Linear Units (GELUs). arXiv: Learning ( 2016 ). Dan Hendrycks and Kevin Gimpel. 2016. Gaussian Error Linear Units (GELUs). arXiv: Learning (2016)."},{"key":"e_1_3_2_2_17_1","unstructured":"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]  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]"},{"key":"e_1_3_2_2_18_1","volume-title":"Shixiang Shane Gu, and Ben Poole","author":"Jang Eric","year":"2017","unstructured":"Eric Jang , Shixiang Shane Gu, and Ben Poole . 2017 . Categorical Reparameterization with Gumbel-Softmax. ArXiv abs\/1611.01144 (2017). Eric Jang, Shixiang Shane Gu, and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. ArXiv abs\/1611.01144 (2017)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347015"},{"key":"e_1_3_2_2_20_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba . 2015 . Adam : A Method for Stochastic Optimization. CoRR abs\/1412.6980 (2015). Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. CoRR abs\/1412.6980 (2015)."},{"key":"e_1_3_2_2_21_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132972"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1014"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411992"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463248"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"Lei Li Yongfeng Zhang and Li Chen. 2021. Personalized Transformer for Explainable Recommendation. In ACL\/IJCNLP.  Lei Li Yongfeng Zhang and Li Chen. 2021. Personalized Transformer for Explainable Recommendation. In ACL\/IJCNLP.","DOI":"10.18653\/v1\/2021.acl-long.383"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080822"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_2_29_1","volume-title":"ROUGE: A Package for Automatic Evaluation of Summaries. In ACL","author":"Lin Chin-Yew","year":"2004","unstructured":"Chin-Yew Lin . 2004 . ROUGE: A Package for Automatic Evaluation of Summaries. In ACL 2004. Chin-Yew Lin. 2004. ROUGE: A Package for Automatic Evaluation of Summaries. In ACL 2004."},{"key":"e_1_3_2_2_30_1","volume-title":"Plug and Play Autoencoders for Conditional Text Generation. arXiv preprint arXiv:2010.02983","author":"Mai Florian","year":"2020","unstructured":"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 ). 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)."},{"key":"e_1_3_2_2_31_1","first-page":"243","article-title":"Rhetorical Structure Theory: Toward a functional theory of text organization","volume":"8","author":"Mann William C.","year":"1988","unstructured":"William C. Mann and Sandra A. Thompson . 1988 . Rhetorical Structure Theory: Toward a functional theory of text organization . Text & Talk 8 (1988), 243 -- 281 . William C. Mann and Sandra A. Thompson. 1988. Rhetorical Structure Theory: Toward a functional theory of text organization. Text & Talk 8 (1988), 243 -- 281.","journal-title":"Text & Talk"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507163"},{"key":"e_1_3_2_2_33_1","unstructured":"Jianmo Ni Jiacheng Li and Julian McAuley. 2019. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In EMNLP.  Jianmo Ni Jiacheng Li and Julian McAuley. 2019. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In EMNLP."},{"key":"e_1_3_2_2_34_1","unstructured":"Sebastian Nowozin Botond Cseke and Ryota Tomioka. 2016. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. In NIPS.  Sebastian Nowozin Botond Cseke and Ryota Tomioka. 2016. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. In NIPS."},{"key":"e_1_3_2_2_35_1","unstructured":"Alec Radford and Karthik Narasimhan. 2018. Improving Language Understanding by Generative Pre-Training  Alec Radford and Karthik Narasimhan. 2018. Improving Language Understanding by Generative Pre-Training"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3291007"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11960"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411949"},{"key":"e_1_3_2_2_39_1","volume-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems -","volume":"2","author":"Sohn Kihyuk","year":"2015","unstructured":"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. 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."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"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.  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.","DOI":"10.18653\/v1\/2021.acl-long.437"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331244"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220086"},{"key":"e_1_3_2_2_43_1","volume-title":"Attention is All you Need. ArXiv abs\/1706.03762","author":"Vaswani Ashish","year":"2017","unstructured":"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 ). 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)."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210010"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"crossref","unstructured":"Yizhong Wang Sujian Li and Jingfeng Yang. 2018. Toward Fast and Accurate Neural Discourse Segmentation. In EMNLP.  Yizhong Wang Sujian Li and Jingfeng Yang. 2018. Toward Fast and Accurate Neural Discourse Segmentation. In EMNLP.","DOI":"10.18653\/v1\/D18-1116"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331203"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2898777"},{"key":"e_1_3_2_2_48_1","volume-title":"Attribute2Image: Conditional Image Generation from Visual Attributes. ArXiv abs\/1512.00570","author":"Yan Xinchen","year":"2016","unstructured":"Xinchen Yan , Jimei Yang , Kihyuk Sohn , and Honglak Lee . 2016. Attribute2Image: Conditional Image Generation from Visual Attributes. ArXiv abs\/1512.00570 ( 2016 ). Xinchen Yan, Jimei Yang, Kihyuk Sohn, and Honglak Lee. 2016. Attribute2Image: Conditional Image Generation from Visual Attributes. ArXiv abs\/1512.00570 (2016)."},{"key":"e_1_3_2_2_49_1","volume-title":"VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders. In IJCAI.","author":"Yu Xianwen","year":"2019","unstructured":"Xianwen Yu , Xiaoning Zhang , Yang Cao , and Min Xia . 2019 . VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders. In IJCAI. Xianwen Yu, Xiaoning Zhang, Yang Cao, and Min Xia. 2019. VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders. In IJCAI."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2600428.2609579"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331390"},{"key":"e_1_3_2_2_52_1","volume-title":"InfoVAE: Information Maximizing Variational Autoencoders. ArXiv abs\/1706.02262","author":"Zhao Shengjia","year":"2017","unstructured":"Shengjia Zhao , Jiaming Song , and Stefano Ermon . 2017. InfoVAE: Information Maximizing Variational Autoencoders. ArXiv abs\/1706.02262 ( 2017 ). Shengjia Zhao, Jiaming Song, and Stefano Ermon. 2017. InfoVAE: Information Maximizing Variational Autoencoders. ArXiv abs\/1706.02262 (2017)."},{"key":"e_1_3_2_2_53_1","unstructured":"Tiancheng Zhao Kyusong Lee and Maxine Esk\u00e9nazi. 2018. Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation. In ACL.  Tiancheng Zhao Kyusong Lee and Maxine Esk\u00e9nazi. 2018. Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation. In ACL."},{"key":"e_1_3_2_2_54_1","volume-title":"Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. arXiv preprint arXiv:1703.10960","author":"Zhao Tiancheng","year":"2017","unstructured":"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 ). 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)."}],"event":{"name":"SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Madrid Spain","acronym":"SIGIR '22","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3532039","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3477495.3532039","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:34Z","timestamp":1750183834000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3532039"}},"subtitle":["A Hierarchical VAE for Personalized Explainable Recommendation."],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":54,"alternative-id":["10.1145\/3477495.3532039","10.1145\/3477495"],"URL":"https:\/\/doi.org\/10.1145\/3477495.3532039","relation":{},"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"2022-07-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}