Conversational Agents for Recommender Systems
Andrea Iovine · 2020
In my Ph.D. work, my objective is to improve the state of the art in Conversational Recommender Systems, by proposing a model that closely follows the process that people enact when searching for products and services. Rich user profiles are elicited using natural language dialogue. Item descriptions will be extracted from a combination of structured and unstructured data such as user reviews. Natural language explanations will ensure that users can quickly understand the reasoning behind the recommendations. Interactive explanation will then allow them to further compare several alternatives. This extended abstract presents the motivations of my work, it details the research plan, and the research questions. Finally, it shows some preliminary results, and outlines the next steps for my Ph.D. program.