Authors:
Aymen Ben Hassen
1
;
Sonia Ben Ticha
2
;
1
and
Anja Habacha Chaibi
1
Affiliations:
1
RIADI Laboratory, University of Manouba, Tunisia
;
2
Borj El Amri Aviation School, Tunisia
Keyword(s):
Deep Learning, Features Extraction, Multimedia Features, Image Feature Extraction, Video Feature Extraction, Recommender Systems, Image Recommendation, Video Recommendation.
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 Filt
ering (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.
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