Research Area:  Machine Learning
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 languages during which they can explicitly ask whether a user likes an attribute or not. With the preferred attributes, a recommender system can conduct more accurate and personalized recommendations. Therefore, while they are still a relatively new topic, conversational recommender systems attract great research attention. We identify four emerging directions: (1) exploration and exploitation trade-off in the cold-start recommendation setting; (2) attribute-centric conversational recommendation; (3) strategy-focused conversational recommendation; and (4) dialogue understanding and response generation. This tutorial covers these four directions, providing a review of existing approaches and progress on the 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.
Author(s) Name:  Wenqiang Lei , Xiangnan He , Maarten de Rijke , Tat-Seng Chua
Conferrence name:  Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher name:  ACM
Volume Information:  July 2020 Pages 2425–2428
Paper Link:   https://dl.acm.org/doi/10.1145/3397271.3401419