In this paper, we investigate the combination of Virtual Assistants and Conversational Recommender Systems (CoRSs) by designing and implementing a framework named ConveRSE, for building chatbots that can recommend items from different domains and interact with the user through natural language. An user experiment was carried out to understand how natural language influences both the cost of interaction and recommendation accuracy of a CoRS. Experimental results show that natural language can indeed improve user experience, but some critical aspects of the interaction should be mitigated appropriately.
An investigation on the impact of natural language on conversational recommendations / Iovine, A.; Narducci, F.; de Gemmis, M.; Semeraro, G.. - 2947:(2021). (Intervento presentato al convegno 11th Italian Information Retrieval Workshop, IIR 2021 tenutosi a Department of Electrical and Information Engineering of Politecnico di Bari, ita nel 2021).
An investigation on the impact of natural language on conversational recommendations
Narducci F.;de Gemmis M.;
2021-01-01
Abstract
In this paper, we investigate the combination of Virtual Assistants and Conversational Recommender Systems (CoRSs) by designing and implementing a framework named ConveRSE, for building chatbots that can recommend items from different domains and interact with the user through natural language. An user experiment was carried out to understand how natural language influences both the cost of interaction and recommendation accuracy of a CoRS. Experimental results show that natural language can indeed improve user experience, but some critical aspects of the interaction should be mitigated appropriately.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
