In this paper, we present a strategy to introduce natural language preference elicitation in a virtual assistant for the movie domain. Our approach allows users to express preferences on objective movie features (e.g., actors, directors, etc.) that are extracted from a structured knowledge base, as well as on subjective features that are collected by mining movie reviews. The effectiveness of the approach was evaluated in a user study (N=103), where our strategy was integrated in a virtual assistant that acquires users' preferences expressed in form of natural language statements and generates a suitable movie recommendation. Results showed that users experience some difficulties in expressing their preferences in terms of subjective features. However, when people succeed in expressing their preferences by also using subjective properties, this generally leads to better recommendations.
A Virtual Assistant for the Movie Domain Exploiting Natural Language Preference Elicitation Strategies / Martina, A. F. M.; Musto, C.; Iovine, A.; De Gemmis, M.; Narducci, F.; Semeraro, G.. - (2022), pp. 7-12. (Intervento presentato al convegno 30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP2022 tenutosi a esp nel 2022) [10.1145/3511047.3536407].
A Virtual Assistant for the Movie Domain Exploiting Natural Language Preference Elicitation Strategies
Musto C.;De Gemmis M.;Narducci F.;
2022-01-01
Abstract
In this paper, we present a strategy to introduce natural language preference elicitation in a virtual assistant for the movie domain. Our approach allows users to express preferences on objective movie features (e.g., actors, directors, etc.) that are extracted from a structured knowledge base, as well as on subjective features that are collected by mining movie reviews. The effectiveness of the approach was evaluated in a user study (N=103), where our strategy was integrated in a virtual assistant that acquires users' preferences expressed in form of natural language statements and generates a suitable movie recommendation. Results showed that users experience some difficulties in expressing their preferences in terms of subjective features. However, when people succeed in expressing their preferences by also using subjective properties, this generally leads to better recommendations.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
