You Get What You Chat: Using Conversations to Personalize Search-based Recommendations
This work addresses the challenge of improving recommendation systems for users by leveraging chat data, though it is incremental as it builds on existing methods with a new data source.
The paper tackles the problem of personalizing search-based recommendations by using implicit signals from online chats instead of explicit user data, achieving performance on par with questionnaire-based profiles in terms of NDCG@20 across domains like books, travel, and food.
Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes, and ratings. This paper investigates tapping into a different source of implicit signals of interests and tastes: online chats between users. The paper develops an expressive model and effective methods for personalizing search-based entity recommendations. User models derived from chats augment different methods for re-ranking entity answers for medium-grained queries. The paper presents specific techniques to enhance the user models by capturing domain-specific vocabularies and by entity-based expansion. Experiments are based on a collection of online chats from a controlled user study covering three domains: books, travel, food. We evaluate different configurations and compare chat-based user models against concise user profiles from questionnaires. Overall, these two variants perform on par in terms of NCDG@20, but each has advantages in certain domains.