Personalized Chit-Chat Generation for Recommendation Using External Chat Corpora
- Changyu Chen ,
- Xiting Wang ,
- Xiaoyuan Yi ,
- Fangzhao Wu ,
- Xing Xie ,
- Rui Yan
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD) |
Applied Data Science Track
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 on 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 for 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.