research-article

Personalized Chit-Chat Generation for Recommendation Using External Chat Corpora

Authors:

Rui YanAuthors Info & Claims

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 2721 - 2731

https://doi.org/10.1145/3534678.3539215

Published: 14 August 2022 Publication History

Abstract

Chit-chat has been shown effective in engaging users in human-computer interaction. We find with a user study that generating appropriate chit-chat for news articles can help expand user interest and increase the probability that a user reads a recommended news article. Based on this observation, we propose a method to generate personalized chit-chat for news recommendation. Different from existing methods for personalized text generation, our method only requires an external chat corpus obtained from an online forum, which can be disconnected from the recommendation dataset from both the user and item (news) perspectives. This is achieved by designing a weak supervision method for estimating users' personalized interest in a chit-chat post by transferring knowledge learned by a news recommendation model. Based on the method for estimating user interest, a reinforcement learning framework is proposed to generate personalized chit-chat. Extensive experiments, including the automatic offline evaluation and user studies, demonstrate the effectiveness of our method.

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Cited By

Bansal NBala MSharma K(2025)Web Personalization with Large Language Models: Challenges and Future TrendsProceedings of International Conference on Paradigms of Communication, Computing and Data Analytics10.1007/978-981-97-8669-5_21(269-283)Online publication date: 27-Jan-2025
https://doi.org/10.1007/978-981-97-8669-5_21
Uchida TMinato TIshiguro H(2024)Opinion attribution improves motivation to exchange subjective opinions with humanoid robotsFrontiers in Robotics and AI10.3389/frobt.2024.117587911Online publication date: 19-Feb-2024
https://doi.org/10.3389/frobt.2024.1175879
Chen JLiu ZHuang XWu CLiu QJiang GPu YLei YChen XWang XZheng KLian DChen E(2024)When large language models meet personalization: perspectives of challenges and opportunitiesWorld Wide Web10.1007/s11280-024-01276-127:4Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1007/s11280-024-01276-1
Show More Cited By

Index Terms

Personalized Chit-Chat Generation for Recommendation Using External Chat Corpora
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Natural language generation
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization

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cover image ACM Conferences

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2022

5033 pages

ISBN:9781450393850

DOI:10.1145/3534678

General Chairs:
Aidong Zhang
University of Virginia
,
Huzefa Rangwala
Amazon/George Mason University

Copyright © 2022 ACM.

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 the author(s) 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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Published: 14 August 2022

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Beijing Outstanding Young Scientist Program award
National Natural Science Foundation of China award

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KDD '22

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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2022

Washington DC, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Bansal NBala MSharma K(2025)Web Personalization with Large Language Models: Challenges and Future TrendsProceedings of International Conference on Paradigms of Communication, Computing and Data Analytics10.1007/978-981-97-8669-5_21(269-283)Online publication date: 27-Jan-2025
https://doi.org/10.1007/978-981-97-8669-5_21
Uchida TMinato TIshiguro H(2024)Opinion attribution improves motivation to exchange subjective opinions with humanoid robotsFrontiers in Robotics and AI10.3389/frobt.2024.117587911Online publication date: 19-Feb-2024
https://doi.org/10.3389/frobt.2024.1175879
Chen JLiu ZHuang XWu CLiu QJiang GPu YLei YChen XWang XZheng KLian DChen E(2024)When large language models meet personalization: perspectives of challenges and opportunitiesWorld Wide Web10.1007/s11280-024-01276-127:4Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1007/s11280-024-01276-1
Chen CWang XJin YDong VDong LCao JLiu YYan RKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Semi-offline reinforcement learning for optimized text generationProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3618608(5087-5103)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3618608

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