Deep Learning for Multimedia Feature Extraction for Personalized Recommendation

Aymen Ben Hassen, Sonia Ben Ticha, Sonia Ben Ticha, Anja Habacha Chaibi

2025

Abstract

The analysis of multimedia content plays a crucial role in various computer vision applications, and digital multimedia constitute a major part of multimedia data. In recent years, multimedia content products have gained increasing attention in recommendation systems since the visual appearance of products has a significant impact on users’ decision. The main goal of personalized recommender systems is to offer users recommendations that reflect with their personal preferences. In recent years, deep learning models have demonstrated strong performance and great potential in utilizing multimedia features, especially for videos and images. This paper presents a new approach that utilizes multimedia content to build a personalized user model. We employ deep learning techniques to extract latent features from multimedia content of item videos, which are then associated with user preferences to build the personalized model. This model is subsequently incorporated into a Collaborative Filtering (CF) to provide recommendations and enhance their accuracy. We experimentally evaluate our approach using the MovieLens dataset and compare our results with those of other methods which deals with different text and images attributes describing items.

Paper Citation

in Harvard Style

Ben Hassen A., Ben Ticha S. and Chaibi A. (2025). Deep Learning for Multimedia Feature Extraction for Personalized Recommendation. In Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-772-6, SciTePress, pages 267-276. DOI: 10.5220/0013718800003985

in Bibtex Style

@conference{webist25,
author={Aymen Ben Hassen and Sonia Ben Ticha and Anja Chaibi},
title={Deep Learning for Multimedia Feature Extraction for Personalized Recommendation},
booktitle={Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2025},
pages={267-276},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013718800003985},
isbn={978-989-758-772-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Deep Learning for Multimedia Feature Extraction for Personalized Recommendation
SN - 978-989-758-772-6
AU - Ben Hassen A.
AU - Ben Ticha S.
AU - Chaibi A.
PY - 2025
SP - 267
EP - 276
DO - 10.5220/0013718800003985
PB - SciTePress