Advances and Challenges in Conversational Recommender Systems: A Survey

Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications. However, static recommendation models are difficult to answer two important questions well due to inherent shortcomings: (a) What exactly does a user like? (b) Why does a user like an item? The shortcomings are due to the way that static models learn user preference, i.e., without explicit instructions and active feedback from users. The recent rise of conversational recommender systems (CRSs) changes this situation fundamentally. In a CRS, users and the system can dynamically communicate through natural language interactions, which provide unprecedented opportunities to explicitly obtain the exact preference of users. Considerable efforts, spread across disparate settings and applications, have been put into developing CRSs. Existing models, technologies, and evaluation methods for CRSs are far from mature. In this paper, we provide a systematic review of the techniques used in current CRSs. We summarize the key challenges of developing CRSs in five directions: (1) Question-based user preference elicitation. (2) Multi-turn conversational recommendation strategies. (3) Dialogue understanding and generation.

Introduction. Recommender systems have become an indispensable tool for information seeking. Companies such as Amazon and Alibaba, in e-commerce, Facebook and Wechat, in social networking, Instagram and Pinterest, in content sharing, and YouTube and Netflix, in multimedia services, all have the need to properly link items (e.g., products, posts, and movies) to users. An effective recommender system that is both accurate and timely can help users find the desired information and bring significant value to the business. Therefore, the development of recommendation techniques continues to attract academic and industrial attention. Traditional recommender systems, which we call static recommendation models in this survey, primarily predict a user’s preference towards an item by analyzing past behaviors offline, e.g., click history, visit log, ratings on items.

Discussion / Conclusion. Recommender systems are playing increasingly important role in information seeking and retrieval. Despite having been studied for decades, traditional recommender systems estimate user preferences only in a static manner like through historical user behaviours and profiles. It offers no opportunities to communicate with users about their preferences. This inevitably suffers from a fundamental information asymmetry problem: a system will never know precisely what a user likes (especially when his/her preference drifts frequently) and why the user likes an item. The envision of Conversational recommender systems (CRSs) brings a promising solution to such problems. With the interactive ability as well as the natural language-based user interface, CRSs can dynamically get explicit user feedback using natural languages, while increasing user engagement and improving user experience. This bold vision provides great potential for the future of recommender system, hence actively contributes to the development of the next generation of information seeking techniques.