An introduction to the special issue on conversational search and recommendation is presented in this article. While conversational search and recommendation has roots in early Information Retrieval (IR) research, the recent advances in automatic voice recognition and conversational agents have created increasing interest in this area. In recent years, the IR and related communities have witnessed a number of major contributions to the field of conversational search and recommendation. They include but are not limited to conversational search conceptualization. The growing body of work in this area has been supplemented by an increasing number of recent seminars.

Recommendation system has became an important component in many real applications, ranging from e-commerce, music app to video-sharing site and on-line book store. The key of a successful recommendation system lies in the accurate user/item profiling. With the advent of web 2.0, quite a lot of multimodal information has been accumulated, which provides us with the opportunity to profile users in a more comprehensive manner. However, directly integrating multimodal information into recommendation system is not a trivial task, because they may be either homogenous or heterogeneous, which requires more advanced method for both fusion and alignment. This workshop aims to provide a platform for discussing the challenges and corresponding innovative approaches in fusing multi-dimensional information for user modeling and recommender systems. We hope more advanced technologies can be proposed or inspired, and also we hope that the direction of integrating different types of information can catch much more attention in both academic and industry.