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Graph-based Extractive Explainer for Recommendations

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Hongning WangAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2163 - 2171

https://doi.org/10.1145/3485447.3512168

Published: 25 April 2022 Publication History

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Abstract

Explanations in a recommender system assist users make informed decisions among a set of recommended items. Extensive research attention has been devoted to generate natural language explanations to depict how the recommendations are generated and why the users should pay attention to them. However, due to different limitations of those solutions, e.g., template-based or generation-based, it is hard to make the explanations easily perceivable, reliable, and personalized at the same time.

In this work, we develop a graph attentive neural network model that seamlessly integrates user, item, attributes and sentences for extraction-based explanation. The attributes of items are selected as the intermediary to facilitate message passing for user-item specific evaluation of sentence relevance. And to balance individual sentence relevance, overall attribute coverage and content redundancy, we solve an integer linear programming problem to make the final selection of sentences. Extensive empirical evaluations against a set of state-of-the-art baseline methods on two benchmark review datasets demonstrated the generation quality of proposed solution.

References

[1]

Charu C Aggarwal 2016. Recommender systems. Vol. 1. Springer.

[2]

Mustafa Bilgic and Raymond J Mooney. 2005. Explaining recommendations: Satisfaction vs. promotion. In Beyond Personalization Workshop, IUI, Vol. 5.

[3]

Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165(2020).

[4]

Renqin Cai, Xueying Bai, Zhenrui Wang, Yuling Shi, Parikshit Sondhi, and Hongning Wang. 2018. Modeling sequential online interactive behaviors with temporal point process. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 873–882.

Digital Library

[5]

Renqin Cai, Chi Wang, and Hongning Wang. 2017. Accounting for the Correspondence in Commented Data. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 365–374.

Digital Library

[6]

Renqin Cai, Qinglei Wang, Chong Wang, and Xiaobing Liu. 2020. Learning to structure long-term dependence for sequential recommendation. arXiv preprint arXiv:2001.11369(2020).

[7]

Renqin Cai, Jibang Wu, Aidan San, Chong Wang, and Hongning Wang. 2021. Category-aware collaborative sequential recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 388–397.

Digital Library

[8]

Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural attentional rating regression with review-level explanations. In Proceedings of the 2018 World Wide Web Conference. 1583–1592.

Digital Library

[9]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018).

[10]

Ondřej Dušek, Jekaterina Novikova, and Verena Rieser. 2020. Evaluating the state-of-the-art of end-to-end natural language generation: The e2e nlg challenge. Computer Speech & Language 59 (2020), 123–156.

Digital Library

[11]

Daniel Fleder and Kartik Hosanagar. 2009. Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Management science 55, 5 (2009), 697–712.

[12]

Jonathan L Herlocker, Joseph A Konstan, and John Riedl. 2000. Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM conference on Computer supported cooperative work. ACM, 241–250.

Digital Library

[13]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).

[14]

Trung-Hoang Le and Hady W Lauw. 2021. Synthesizing aspect-driven recommendation explanations from reviews. IJCAI.

[15]

Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, and Libing Wu. 2019. A Capsule Network for Recommendation and Explaining What You Like and Dislike. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 275–284.

Digital Library

[16]

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. 755–764.

Digital Library

[17]

Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. 2017. Neural rating regression with abstractive tips generation for recommendation. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 345–354.

Digital Library

[18]

Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out. 74–81.

[19]

Weizhi Ma, Min Zhang, Yue Cao, Woojeong Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, and Xiang Ren. 2019. Jointly learning explainable rules for recommendation with knowledge graph. In The World Wide Web Conference. 1210–1221.

Digital Library

[20]

Julian McAuley, Jure Leskovec, and Dan Jurafsky. 2012. Learning attitudes and attributes from multi-aspect reviews. In 2012 IEEE 12th International Conference on Data Mining. IEEE, 1020–1025.

Digital Library

[21]

Silvia Milano, Mariarosaria Taddeo, and Luciano Floridi. 2020. Recommender systems and their ethical challenges. AI & SOCIETY 35, 4 (2020), 957–967.

Digital Library

[22]

Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 188–197.

[23]

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 311–318.

[24]

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. In Empirical Methods in Natural Language Processing (EMNLP). 1532–1543. http://www.aclweb.org/anthology/D14-1162

[25]

Reinald Adrian Pugoy and Hung-Yu Kao. 2021. Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2981–2990.

[26]

Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. (2018).

[27]

Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084(2019).

[28]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. ”Why should i trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1135–1144.

Digital Library

[29]

Amit Sharma and Dan Cosley. 2013. Do social explanations work?: studying and modeling the effects of social explanations in recommender systems. In Proceedings of the 22nd international conference on World Wide Web. 1133–1144.

Digital Library

[30]

Rashmi Sinha and Kirsten Swearingen. 2002. The role of transparency in recommender systems. In CHI’02 extended abstracts on Human factors in computing systems. ACM, 830–831.

[31]

Kirsten Swearingen and Rashmi Sinha. 2001. Beyond algorithms: An HCI perspective on recommender systems. In ACM SIGIR 2001 Workshop on Recommender Systems, Vol. 13. Citeseer, 1–11.

[32]

Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos. 2008. Providing justifications in recommender systems. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 38, 6(2008), 1262–1272.

Digital Library

[33]

Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang. 2021. Counterfactual explainable recommendation. arXiv preprint arXiv:2108.10539(2021).

[34]

Yiyi Tao, Yiling Jia, Nan Wang, and Hongning Wang. 2019. The FacT: Taming Latent Factor Models for Explainability with Factorization Trees. In Proceedings of the 42Nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY, USA, 295–304.

Digital Library

[35]

Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903(2017).

[36]

Hongning Wang, Yue Lu, and Chengxiang Zhai. 2010. Latent aspect rating analysis on review text data: a rating regression approach. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 783–792.

Digital Library

[37]

Nan Wang, Hongning Wang, Yiling Jia, and Yue Yin. 2018. Explainable recommendation via multi-task learning in opinionated text data. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 165–174.

Digital Library

[38]

Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017. Deep & cross network for ad click predictions. In Proceedings of the ADKDD’17. 1–7.

Digital Library

[39]

Xiting Wang, Yiru Chen, Jie Yang, Le Wu, Zhengtao Wu, and Xing Xie. 2018. A reinforcement learning framework for explainable recommendation. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 587–596.

[40]

Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. 2019. Explainable reasoning over knowledge graphs for recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 5329–5336.

Digital Library

[41]

Jibang Wu, Renqin Cai, and Hongning Wang. 2020. Déjà vu: A contextualized temporal attention mechanism for sequential recommendation. In Proceedings of The Web Conference 2020. 2199–2209.

Digital Library

[42]

Yikun Xian, Zuohui Fu, Shan Muthukrishnan, Gerard De Melo, and Yongfeng Zhang. 2019. Reinforcement knowledge graph reasoning for explainable recommendation. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval. 285–294.

Digital Library

[43]

Yikun Xian, Tong Zhao, Jin Li, Jim Chan, Andrey Kan, Jun Ma, Xin Luna Dong, Christos Faloutsos, George Karypis, Shan Muthukrishnan, 2021. EX3: Explainable Attribute-aware Item-set Recommendations. In Fifteenth ACM Conference on Recommender Systems. 484–494.

Digital Library

[44]

Aobo Yang, Nan Wang, Renqin Cai, Hongbo Deng, and Hongning Wang. 2021. Comparative Explanations of Recommendations. arXiv preprint arXiv:2111.00670(2021).

[45]

Aobo Yang, Nan Wang, Hongbo Deng, and Hongning Wang. 2021. Explanation as a Defense of Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 1029–1037.

Digital Library

[46]

Yongfeng Zhang and Xu Chen. 2018. Explainable recommendation: A survey and new perspectives. arXiv preprint arXiv:1804.11192(2018).

[47]

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. 83–92.

Digital Library

[48]

Yongfeng Zhang, Haochen Zhang, Min Zhang, Yiqun Liu, and Shaoping Ma. 2014. Do users rate or review? Boost phrase-level sentiment labeling with review-level sentiment classification. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. 1027–1030.

Cited By

Yu YSugiyama KJatowt AHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Sequential Recommendation with Collaborative Explanation via Mutual Information MaximizationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657770(1062-1072)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657770
Barkan OBogina VGurevitch LAsher YKoenigstein NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Counterfactual Framework for Learning and Evaluating Explanations for Recommender SystemsProceedings of the ACM on Web Conference 202410.1145/3589334.3645560(3723-3733)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645560
Yan HGui LWang MZhang KHe Y(2024)Explainable Recommender With Geometric Information BottleneckIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.335044736:7(3036-3046)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3350447
Show More Cited By

Index Terms

Graph-based Extractive Explainer for Recommendations
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 25 April 2022

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WWW '22

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Yu YSugiyama KJatowt AHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Sequential Recommendation with Collaborative Explanation via Mutual Information MaximizationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657770(1062-1072)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657770
Barkan OBogina VGurevitch LAsher YKoenigstein NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Counterfactual Framework for Learning and Evaluating Explanations for Recommender SystemsProceedings of the ACM on Web Conference 202410.1145/3589334.3645560(3723-3733)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645560
Yan HGui LWang MZhang KHe Y(2024)Explainable Recommender With Geometric Information BottleneckIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.335044736:7(3036-3046)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3350447
Li LZhang YChen L(2023)On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved PerformanceACM Transactions on Intelligent Systems and Technology10.1145/356942314:2(1-24)Online publication date: 16-Feb-2023
https://dl.acm.org/doi/10.1145/3569423
Shuai JWu LZhang KSun PHong RWang MChen HDuh WHuang HKato MMothe JPoblete B(2023)Topic-enhanced Graph Neural Networks for Extraction-based Explainable RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591776(1188-1197)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591776
Sun NKou YZhou XShen DLi DNie T(2023)Learning Implicit Sentiment for Explainable Review-Based RecommendationDatabases Theory and Applications10.1007/978-3-031-47843-7_5(59-72)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47843-7_5
Zuo XJia THe XYang BWang Y(2022)Exploiting Dual-Attention Networks for Explainable Recommendation in Heterogeneous Information NetworksEntropy10.3390/e2412171824:12(1718)Online publication date: 24-Nov-2022
https://doi.org/10.3390/e24121718

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