A Framework for Users’ Attitude Prediction Based on Visual Features: Application to Movies Recommendations

Hajer Baazaoui () and Mohamed Ramzi Haddad ()
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Hajer Baazaoui: Riadi Laboratory, ENSI, University of Manouba, Tunisia
Mohamed Ramzi Haddad: Riadi Laboratory, ENSI, University of Manouba, Tunisia

Journal of Information & Knowledge Management (JIKM), 2018, vol. 17, issue 04, 1-18

Abstract: Recommendation and personalisation approaches aim to filter the most interesting resources that may attract users’ personal interests and preferences by analysing their past attitudes and consumption patterns. The visual aspect of content constitutes an important factor that drives consumers’ attitudes and decisions. In this context, this work proposes a framework for users’ attitude prediction based on items’ visual descriptors and details one of its possible applications for movies recommendations. The main idea of our proposal is to model users’ interests and consumption behaviors using the movies’ posters images and extract features based on the visual descriptors of the items that they interact with, in order to better predict their attitudes towards the ones they do not know. The recommendation approach was integrated into a movies recommendation application that visually assists users while searching for relevant movies to watch, by finding similar movie posters based on the visual aspects of the poster image.

Keywords: User modelling; recommendation; visual description; visual interests learning; prediction (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S0219649218500375

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