Personalized Chit-Chat Generation for Recommendation Using External Chat Corpora
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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More recent data-driven methods incorporate user feedback as rewards in a reinforcement learning framework to guide the generation process.
“More recent data-driven methods have emerged, which incorporate user feedback as rewards in the reinforcement learning framework to guide the generation process [259-262].”
From When large language models meet personalization: perspectives of challenges and opportunities (2024) · cites this paper as [261]
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- Authors
- Chen, C., Wang, X., Yi, X., Wu, F., Xie, X., & Yan, R.
- Affiliations
- Renmin University of China (CN)
- Microsoft Research Asia (China) (CN)
- Venue
- Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2022-08-14
- Pages
- 2721-2731
- OpenAlex
- W4290875552
- Cited by
- 5
- References
- 21
- Citations by year
- 2025: 3 · 2024: 2
- FWCI
- 1.12
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- Beijing Outstanding Young Scientist Program award (NO. BJJWZYJH012019100020098)
- National Natural Science Foundation of China award ((NSFC Grant No. 62122089 and No. 61876196))
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- personalized recommendation, news recommendation, online forums, weakly supervised learning, user interest estimation, knowledge transfer, reinforcement learning, user engagement, human-computer interaction
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