Hybrid Recommender System Using Emotional Fingerprints Model

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Author(s): Anthony Nosshi (Mansoura University, Mansoura, Egypt), Aziza Saad Asem (Mansoura University, Mansoura, Egypt)and Mohammed Badr Senousy (Sadat Academy for Management Sciences, Cairo, Egypt)
Copyright: 2022
Pages: 25
Source title: Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-6303-1.ch056

Keywords: Data Analysis and Statistics / Data Mining / Engineering Science Reference / Library & Information Science

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Abstract

With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.

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Hybrid Recommender System Using Emotional Fingerprints Model

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