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Generate Neural Template Explanations for Recommendation

Published: 19 October 2020 Publication History

Abstract

Personalized recommender systems are important to assist user decision-making in the era of information overload. Meanwhile, explanations of the recommendations further help users to better understand the recommended items so as to make informed choices, which gives rise to the importance of explainable recommendation research. Textual sentence-based explanation has been an important form of explanations for recommender systems due to its advantage in communicating rich information to users. However, current approaches to generating sentence explanations are either limited to predefined sentence templates, which restricts the sentence expressiveness, or opt for free-style sentence generation, which makes it difficult for sentence quality control. In an attempt to benefit both sentence expressiveness and quality, we propose a Neural Template (NETE) explanation generation framework, which brings the best of both worlds by learning sentence templates from data and generating template-controlled sentences that comment about specific features. Experimental results on real-world datasets show that NETE consistently outperforms state-of-the-art explanation generation approaches in terms of sentence quality and expressiveness. Further analysis on case study also shows the advantages of NETE on generating diverse and controllable explanations.

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Presentation video for the paper Generate Neural Template Explanations for Recommendation, CIKM'20

References

[1]
John Arevalo, Thamar Solorio, Manuel Montes-y Gómez, and Fabio A González. 2017. Gated multimodal units for information fusion. In ICLR Workshop.
[2]
Ziqiang Cao, Wenjie Li, Sujian Li, and Furu Wei. 2018. Retrieve, rerank and rewrite: Soft template based neural summarization. In ACL. 152--161.
[3]
Rose Catherine and William Cohen. 2017. Transnets: Learning to Transform for Recommendation. In RecSys. ACM, 288--296.
[4]
Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural Attentional Rating Regression with Review-level Explanations. In WWW. ACM, 1583--1592.
[5]
Hanxiong Chen, Xu Chen, Shaoyun Shi, and Yongfeng Zhang. 2019 a. Generate Natural Language Explanations for Recommendation. In SIGIR Workshop EARS.
[6]
Li Chen and Feng Wang. 2017. Explaining recommendations based on feature sentiments in product reviews. In IUI. ACM, 17--28.
[7]
Xu Chen, Han Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2019 b. Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation. In SIGIR. ACM, 765--774.
[8]
Xu Chen, Yongfeng Zhang, and Zheng Qin. 2019 d. Dynamic Explainable Recommendation based on Neural Attentive Models. In AAAI.
[9]
Zhongxia Chen, Xiting Wang, Xing Xie, Tong Wu, Guoqing Bu, Yining Wang, and Enhong Chen. 2019 c. Co-Attentive Multi-Task Learning for Explainable Recommendation. In IJCAI.
[10]
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. In EMNLP. 1724--1734.
[11]
Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, and Ke Xu. 2017. Learning to Generate Product Reviews from Attributes. In EACL, Vol. 1. 623--632.
[12]
Jingyue Gao, Xiting Wang, Yasha Wang, and Xing Xie. 2019. Explainable Recommendation Through Attentive Multi-View Learning. In AAAI.
[13]
Fatih Gedikli, Dietmar Jannach, and Mouzhi Ge. 2014. How should I explain? A comparison of different explanation types for recommender systems. International Journal of Human-Computer Studies, Vol. 72, 4 (2014), 367--382.
[14]
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li. 2016. Incorporating copying mechanism in sequence-to-sequence learning. In ACL.
[15]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. ACM, 173--182.
[16]
Jonathan L Herlocker, Joseph A Konstan, and John Riedl. 2000. Explaining collaborative filtering recommendations. In CSCW. ACM, 241--250.
[17]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural computation, Vol. 9, 8 (1997), 1735--1780.
[18]
Min Hou, Le Wu, Enhong Chen, Zhi Li, Vincent W Zheng, and Qi Liu. 2019. Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach. In IJCAI.
[19]
Diederick P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR.
[20]
Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD. ACM, 426--434.
[21]
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 8 (2009), 30--37.
[22]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In NIPS. 1097--1105.
[23]
Johannes Kunkel, Tim Donkers, Lisa Michael, Catalin-Mihai Barbu, and Jürgen Ziegler. 2019. Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems. In CHI. ACM, 487.
[24]
Lei Li, Li Chen, and Yongfeng Zhang. 2020 a. Towards Controllable Explanation Generation for Recommender Systems via Neural Template. In WWW Demo.
[25]
Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. 2017. Neural Rating Regression with Abstractive Tips Generation for Recommendation. In SIGIR. ACM, 345--354.
[26]
Xueqi Li, Wenjun Jiang, Weiguang Chen, Jie Wu, Guojun Wang, and Kenli Li. 2020 b. Directional and Explainable Serendipity Recommendation. In WWW. 122--132.
[27]
Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out. 74--81.
[28]
Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective approaches to attention-based neural machine translation. In EMNLP. 1412--1421.
[29]
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In NIPS. 3111--3119.
[30]
Lili Mou, Yiping Song, Rui Yan, Ge Li, Lu Zhang, and Zhi Jin. 2016. Sequence to backward and forward sequences: A content-introducing approach to generative short-text conversation. In COLING. 3349--3358.
[31]
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In ACL. 311--318.
[32]
Georgina Peake and Jun Wang. 2018. Explanation mining: Post hoc interpretability of latent factor models for recommendation systems. In KDD. ACM.
[33]
Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: an open architecture for collaborative filtering of netnews. In CSCW. ACM, 175--186.
[34]
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In WWW. ACM, 285--295.
[35]
Harald Steck. 2013. Evaluation of recommendations: rating-prediction and ranking. In RecSys. 213--220.
[36]
Nava Tintarev and Judith Masthoff. 2015. Explaining Recommendations: Design and Evaluation. In Recommender Systems Handbook 2 ed.), Bracha Shapira (Ed.). Springer, Chapter 10, 353--382.
[37]
Quoc-Tuan Truong and Hady Lauw. 2019. Multimodal Review Generation for Recommender Systems. In WWW. ACM, 1864--1874.
[38]
Nan Wang, Hongning Wang, Yiling Jia, and Yue Yin. 2018b. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data. In SIGIR. ACM.
[39]
Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, and Xing Xie. 2018a. A Reinforcement Learning Framework for Explainable Recommendation. In ICDM.
[40]
Zhongqing Wang and Yue Zhang. 2017. Opinion Recommendation using Neural Memory Model. In EMNLP. 1627--1638.
[41]
Sam Wiseman, Stuart M Shieber, and Alexander M Rush. 2018. Learning neural templates for text generation. In EMNLP. 3174--3187.
[42]
Lili Yao, Yaoyuan Zhang, Yansong Feng, Dongyan Zhao, and Rui Yan. 2017. Towards implicit content-introducing for generative short-text conversation systems. In EMNLP. 2190--2199.
[43]
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), Vol. 52, 1 (2019), 1--38.
[44]
Yongfeng Zhang and Xu Chen. 2020. Explainable Recommendation: A Survey and New Perspectives. Foundations and Trends® in Information Retrieval, Vol. 14, 1 (2020), 1--101.
[45]
Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014a. Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis. In SIGIR. ACM, 83--92.
[46]
Yongfeng Zhang, Haochen Zhang, Min Zhang, Yiqun Liu, and Shaoping Ma. 2014b. Do users rate or review? Boost phrase-level sentiment labeling with review-level sentiment classification. In SIGIR. ACM, 1027--1030.
[47]
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. ACM, 2425--2435.
[48]
Lei Zheng, Vahid Noroozi, and Philip S Yu. 2017. Joint Deep Modeling of Users and Items Using Reviews for Recommendation. In WSDM. ACM, 425--434.

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cover image ACM Conferences
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
October 2020
3619 pages
ISBN:9781450368599
DOI:10.1145/3340531
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 19 October 2020

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Author Tags

  1. explainable recommendation
  2. natural language generation
  3. neural template explanation
  4. recommender systems

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  • Research-article

Funding Sources

  • HKBU IRCMS Project (IRCMS/19-20/D05)
  • NSF IIS-1910154

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CIKM '20
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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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  • (2025)IReGNN: Implicit review-enhanced graph neural network for explainable recommendationKnowledge-Based Systems10.1016/j.knosys.2025.113113311(113113)Online publication date: Feb-2025
  • (2024)Enhancing Explainable Recommendations: Integrating Reason Generation and Rating Prediction through Multi-Task LearningApplied Sciences10.3390/app1418830314:18(8303)Online publication date: 14-Sep-2024
  • (2024)Review-Based Explainable Recommendations: A Transparency PerspectiveACM Transactions on Recommender Systems10.1145/3701762Online publication date: 30-Oct-2024
  • (2024)Question-Attentive Review-Level Explanation for Neural Rating RegressionACM Transactions on Intelligent Systems and Technology10.1145/369951615:6(1-25)Online publication date: 8-Oct-2024
  • (2024)Aspect-Enhanced Explainable Recommendation with Multi-modal Contrastive LearningACM Transactions on Intelligent Systems and Technology10.1145/367323416:1(1-24)Online publication date: 19-Jun-2024
  • (2024)Improving Faithfulness and Factuality with Contrastive Learning in Explainable RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365398416:1(1-23)Online publication date: 26-Dec-2024
  • (2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
  • (2024)Revisiting Bundle Recommendation for Intent-aware Product BundlingACM Transactions on Recommender Systems10.1145/3652865Online publication date: 15-Mar-2024
  • (2024)A Comparative Analysis of Text-Based Explainable Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688069(105-115)Online publication date: 8-Oct-2024
  • (2024)Natural Language Explainable Recommendation with Robustness EnhancementProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671781(4203-4212)Online publication date: 25-Aug-2024
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