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SIGIR 2019 Tutorial on Explainable Recommendation and Search

Published: 18 July 2019 Publication History

Abstract

Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also intuitive explanations of the results for users or system designers, which can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The tutorial focuses on the research and application of explainable recommendation and search algorithms, as well as their application in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating explainable recommendation and search methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.

References

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Q. Ai, V. Azizi, X. Chen, and Y. Zhang. 2018. Learning Heterogenous Knowledge base Embeddings for Explainable Recommendation. Algorithms (2018).
[2]
Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. 2016. Learning to rank features for recommendation over multiple categories. In SIGIR. ACM, 305--314.
[3]
X. Chen, H. Xu, Y. Zhang, J. Tang, Y. Cao, H. Zha, and Z. Qin. 2018. Sequential Recommendation with User Memory Networks. In WSDM. ACM.
[4]
Xu Chen, Yongfeng Zhang, and Zheng Qin. 2019 a. Dynamic Explainable Recommendation based on Neural Attentive Models. AAAI (2019).
[5]
Xu Chen, Yongfeng Zhang, Hongteng Xu, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2019 b. Visually Explainable Recommendation. SIGIR (2019).
[6]
Y. Xian, Z. Fu, S. Muthukrishnan, G. de Melo, and Y. Zhang. 2019. Reinforcement Knowledge Graph Reasoning for Explainable Recommendation. SIGIR (2019).
[7]
Yongfeng Zhang. 2015. Incorporating phrase-level sentiment analysis on textual reviews for personalized recommendation. In WSDM. ACM, 435--440.
[8]
Yongfeng Zhang. 2017. Explainable Recommendation: Theory and Applications. arXiv preprint arXiv:1708.06409 (2017).
[9]
Yongfeng Zhang and Xu Chen. 2018. Explainable Recommendation: A Survey and New Perspectives. arXiv preprint arXiv:1804.11192 (2018).
[10]
Y. Zhang, G. Lai, M. Zhang, et almbox. 2014a. Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis. SIGIR (2014).
[11]
Yongfeng Zhang, Jiaxin Mao, and Qingyao Ai. 2019. The Web Conference 2019 Tutorial on Explainable Recommendation and Search. WWW (2019).
[12]
Y. Zhang, H. Zhang, et almbox. 2014b. Do Users Rate or Review? Boost Phrase-level Sentiment Labeling with Review-level Sentiment Classification. SIGIR (2014).
[13]
Y. Zhang, M. Zhang, Y. Zhang, G. Lai, Y. Liu, et almbox. 2015. Daily-aware personalized recommendation based on feature-level time series analysis. In WWW.
[14]
Y. Zhang, Y. Zhang, and M. Zhang. 2018. Report on EARS'18: 1st International Workshop on ExplainAble Recommendation and Search. SIGIR Forum (2018).
[15]
Yongfeng Zhang, Yi Zhang, and Min Zhang. 2018. SIGIR 2018 Workshop on ExplainAble Recommendation and Search (EARS 2018). SIGIR (2018).

Cited By

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  • (2023)Explainability of Text Processing and Retrieval MethodsProceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3632754.3632944(153-157)Online publication date: 15-Dec-2023
  • (2023)Explainable Information RetrievalProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3594249(3448-3451)Online publication date: 19-Jul-2023
  • (2022)PEVAEProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532039(692-702)Online publication date: 6-Jul-2022
  • Show More Cited By

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Published In

cover image ACM Conferences
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2019
1512 pages
ISBN:9781450361729
DOI:10.1145/3331184
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2019

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Author Tags

  1. explainable AI
  2. explainable recommendation
  3. explainable search

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  • Tutorial

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SIGIR '19
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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2023)Explainability of Text Processing and Retrieval MethodsProceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3632754.3632944(153-157)Online publication date: 15-Dec-2023
  • (2023)Explainable Information RetrievalProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3594249(3448-3451)Online publication date: 19-Jul-2023
  • (2022)PEVAEProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532039(692-702)Online publication date: 6-Jul-2022
  • (2019)Tutorial on Explainable Recommendation and SearchProceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3341981.3353768(255-256)Online publication date: 26-Sep-2019

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