RecSys 2020 Tutorial: Conversational Recommender Systems

Conversational Recommender Systems
by Yongfeng Zhang (Rutgers University), Zuohui Fu (Rutgers University), Yikun Xian (Rutgers University), and Yi Zhang (University of California Santa Cruz)

Recent years have witnessed the emerging of conversational systems, including both physical devices and mobile-based applications. Both the research community and industry believe that conversational systems will have a major impact on human-computer interaction, and specifically, the RecSys community has begun to explore Conversational Recommendation Systems.

Conversational recommendation aims at finding or recommending the most relevant information (e.g., web pages, answers, movies, products) for users based on textual- or spoken-dialogs, through which users can communicate with the system more efficiently using natural language conversations. Due to users’ constant need to look for information to support both work and daily life, conversational recommendation system will be one of the key techniques towards an intelligent web.

The tutorial focuses on the foundations and algorithms for conversational recommendation, as well as their application in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating conversational recommendation methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.