Transfer Learning to Extract Features for Personalized User Modeling

Aymen Ben Hassen, Sonia Ben Ticha

2020

Abstract

Personalized Recommender Systems help users to choose relevant resources and items from many choices, which is an important challenge that remains actuality today. In recent years, we have witnessed the success of deep learning in several research areas such as computer vision, natural language processing, and image processing. In this paper, we present a new approach exploiting the images describing items to build a new user’s personalized model. With this aim, we use deep learning to extract latent features describing images. Then we associate these features with user preferences to build the personalized model. This model was used in a Collaborative Filtering (CF) algorithm to make recommendations. We apply our approach to real data, the MoviesLens dataset, and we compare our results to other approaches based on collaborative filtering algorithms.

Paper Citation

in Harvard Style

Ben Hassen A. and Ben Ticha S. (2020). Transfer Learning to Extract Features for Personalized User Modeling.In Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-478-7, pages 15-25. DOI: 10.5220/0010109400150025

in Bibtex Style

@conference{webist20,
author={Aymen Ben Hassen and Sonia Ben Ticha},
title={Transfer Learning to Extract Features for Personalized User Modeling},
booktitle={Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2020},
pages={15-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010109400150025},
isbn={978-989-758-478-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Transfer Learning to Extract Features for Personalized User Modeling
SN - 978-989-758-478-7
AU - Ben Hassen A.
AU - Ben Ticha S.
PY - 2020
SP - 15
EP - 25
DO - 10.5220/0010109400150025