Print Email Facebook Twitter Personalized conversational recommender system in a movie platform: the impact on user satisfaction Title Personalized conversational recommender system in a movie platform: the impact on user satisfaction Author Feliciotti, Francesca Feliciotti (TU Delft Technology, Policy and Management) Contributor Rook, L. (mentor) Degree granting institution Delft University of Technology Programme Management of Technology (MoT) Date 2019-09-26 Abstract During the last years, as virtual assistants such as Siri (Apple), Google Assistant, Amazon Alexa, spread into everyday situations, conversational recommender systems were proposed as an interactive recommendation process to connect with the user. However, there is little knowledge about the personalization of conversational recommender systems as a way to increase the satisfaction of the users. The current research focuses on the users’ experience with a movie platform. It argues that users satisfaction can only be improved if the conversational recommender system knows the preferences and the Openness to Experience of the users, and therefore gives custom-made recommendations. Specifically, the relationship between the degree of Openness to Experience and serendipitous or accurate recommendations is investigated. The present study demonstrates that overall people low on Openness to Experience significantly prefer accurate suggestions rather than serendipitous ones. Instead, people high on Openness to Experience do not have a significant preference regarding accurate or serendipitous recommendations. Furthermore, to explore whether a conversational recommender system increases user satisfaction, this study explores to what extent the satisfaction of users in recommender system is dependent on (traditional vs. conversational) recommender system mode of interaction. Results indicate that conversational recommender system exerts a positive impact on user satisfaction. Subject conversational recommender systemOpenness to Experiencerecommendations To reference this document use: http://resolver.tudelft.nl/uuid:97c2a21d-0227-4c43-9751-e1959a5a88a2 Part of collection Student theses Document type master thesis Rights © 2019 Francesca Feliciotti Feliciotti Files PDF Msc_thesis_F.Feliciotti.pdf 1.71 MB Close viewer /islandora/object/uuid:97c2a21d-0227-4c43-9751-e1959a5a88a2/datastream/OBJ/view