RecSys 2021 Tutorial on Conversational Recommendation: Formulation, Methods, and Evaluation

Recommender systems have demonstrated great success in information seeking. However, traditional recommender systems work in a static way, estimating user preferences on items from past interaction history. This prevents recommender systems from capturing dynamic and fine-grained preferences of users. Conversational recommender systems bring a revolution to existing recommender systems. They are able to communicate with users through natural language, which enables them to explicitly elicit user preferences by asking whether a user likes an attribute or item or not. Based on information shared through users’ responses, a recommender system can produce more accurate and personalized recommendations. We identify five emerging trends in the general area of conversational recommender systems: (1) Question-based user preference elicitation; (2) Multi-turn conversational recommendation strategies; (3) Dialogue understanding and generation; (4) Exploitation-exploration trade-offs; and (5) Evaluation and user simulation. This tutorial covers these five directions, providing a review of existing approaches and progress on each topic. By presenting the emerging and promising topic of conversational recommender systems, we aim to provide take-aways to practitioners to build their own systems. We also want to stimulate more ideas and discussions with audiences on core problems of this topic such as task formalization, dataset collection, algorithm development, and evaluation, with the ambition of facilitating the development of conversational recommender systems.