Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

In this paper, we present empirical studies on conversational recommendation tasks using
representative large language models in a zero-shot setting with three primary …

Conversational recommender systems aim to interactively support online users in their
information search and decision-making processes in an intuitive way. With the latest …

Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and better control …

Conversational recommender systems (CRS) have become popular in recent years and
have gained the attention of researchers. More emphasis is given to user choices and …

Modern recommender systems usually embed users and items into a learned vector space
representation. Similarity in this space is used to generate recommendations, and …

Critiquing is a method for conversational recommendation that iteratively adapts
recommendations in response to user preference feedback. In this setting, a user is …

Providing explanations for recommended items not only allows users to understand the
reason for receiving recommendations but also provides users with an opportunity to refine …

A key distinguishing feature of conversational recommender systems over traditional
recommender systems is their ability to elicit user preferences using natural language …