Research Area:  Machine Learning

Abstract:

Tools that interact vocally with users are becoming increasingly popular in the market, boosting industry and academia interest in them. In such environments, conversational recommender systems succeed in guiding users in situations of information overload. Through multiple interactions with users, such systems ask questions, filter the catalog in a personalized manner, and suggest items that are of potential interest to the consumer. In this context, conversational efficiency in terms of the number of required interactions often plays a fundamental role. This work introduces a theoretical and domain independent approach to support the efficiency analysis of a conversational recommendation engine. Observations from an empirical analysis align with our theoretical findings.

Keywords:  
Conversational Recommender Systems
Conversational Recommendation Engine
Recommender Systems
Conversational recommendation

Author(s) Name:  Tommaso Di Noia, Francesco Maria Donini, Dietmar Jannach, Fedelucio Narducci1 and Claudio Pomo

Publisher name:  CEUR

DOI:  

Volume Information:  

Paper Link:   https://ceur-ws.org/Vol-3318/short29.pdf