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Personalized Reason Generation for Explainable Song Recommendation

Authors:

Xueming QianAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 10, Issue 4

Article No.: 41, Pages 1 - 21

https://doi.org/10.1145/3337967

Published: 10 July 2019 Publication History

Abstract

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as “Customers who bought this item also bought…”. Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. The goal of our research is to make the users feel as if they are receiving recommendations from their friends. To this end, we formulate a new challenging problem called personalized reason generation for explainable recommendation for songs in conversation applications and propose a solution that generates a natural language explanation of the reason for recommending a song to that particular user. For example, if the user is a student, our method can generate an output such as “Campus radio plays this song at noon every day, and I think it sounds wonderful,” which the student may find easy to relate to. In the offline experiments, through manual assessments, the gain of our method is statistically significant on the relevance to songs and personalization to users comparing with baselines. Large-scale online experiments show that our method outperforms manually selected reasons by 8.2% in terms of click-through rate. Evaluation results indicate that our generated reasons are relevant to songs and personalized to users, and they attract users to click the recommendations.

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Cited By

Liu QZhu JYang YDai QDu ZWu XZhao ZZhang RDong ZBaeza-Yates RBonchi F(2024)Multimodal Pretraining, Adaptation, and Generation for Recommendation: A SurveyProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671473(6566-6576)Online publication date: 25-Aug-2024
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Wu YZhao GLi MZhang ZQian X(2024)Reason Generation for Point of Interest Recommendation Via a Hierarchical Attention-Based Transformer ModelIEEE Transactions on Multimedia10.1109/TMM.2023.333588626(5511-5522)Online publication date: 1-Jan-2024
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Index Terms

Personalized Reason Generation for Explainable Song Recommendation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Natural language generation
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Internet communications tools
        Chat
    2. Web searching and information discovery
      1. Personalization
      2. Social recommendation

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Published In

ACM Transactions on Intelligent Systems and Technology Volume 10, Issue 4

Survey Papers and Regular Papers

July 2019

327 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3344873

Editor:
Yu Zheng
JD Finance, China

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Copyright © 2019 ACM.

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Publication History

Published: 10 July 2019

Accepted: 01 May 2019

Revised: 01 April 2019

Received: 01 October 2018

Published in TIST Volume 10, Issue 4

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https://dl.acm.org/doi/10.1145/3637528.3671473
Wu YZhao GLi MZhang ZQian X(2024)Reason Generation for Point of Interest Recommendation Via a Hierarchical Attention-Based Transformer ModelIEEE Transactions on Multimedia10.1109/TMM.2023.333588626(5511-5522)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3335886
Pugoy RKao H(2024)NEAR: Non-Supervised Explainability Architecture for Accurate Review-Based Collaborative FilteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322618936:2(750-765)Online publication date: 1-Feb-2024
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Lin YZhang WLin FZeng WZhou XWu P(2024)Knowledge-aware reasoning with self-supervised reinforcement learning for explainable recommendation in MOOCsNeural Computing and Applications10.1007/s00521-023-09257-736:8(4115-4132)Online publication date: 1-Mar-2024
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Motamedi ETkalčič M(2023)User-centric item characteristics for personalized multimedia systems: A systematic reviewIntelligenza Artificiale10.3233/IA-230039(1-22)Online publication date: 15-Dec-2023
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