Conversational Recommendation: A Grand AI Challenge

TL;DR

This paper reviews the current state and challenges of developing conversational recommender systems, emphasizing their potential to make AI recommendations more interactive, personalized, and human-like.

Contribution

It provides a comprehensive overview of existing approaches, current developments, and open challenges in building conversational recommender systems as a major AI research challenge.

Findings

Current systems lack natural conversation capabilities

Development of explainable recommendations is ongoing

Open challenges include memory and personalization issues

Abstract

Animated avatars, which look and talk like humans, are iconic visions of the future of AI-powered systems. Through many sci-fi movies we are acquainted with the idea of speaking to such virtual personalities as if they were humans. Today, we talk more and more to machines like Apple's Siri, e.g., to ask them for the weather forecast. However, when asked for recommendations, e.g., for a restaurant to go to, the limitations of such devices quickly become obvious. They do not engage in a conversation to find out what we might prefer, they often do not provide explanations for what they recommend, and they may have difficulties remembering what was said one minute earlier. Conversational recommender systems promise to address these limitations. In this paper, we review existing approaches to build such systems, which developments we observe today, which challenges are still open and why the development of conversational recommenders represents one of the next grand challenges of AI.