Improving Social Image Recommendations through Emotion Analysis

AhmadKhani, Somaieh; Ebrahimi Moghaddam, Mohsen

doi:10.48308/jicse.2025.237528.1042

Improving Social Image Recommendations through Emotion Analysis

Document Type : Original Article

Authors

Somaieh AhmadKhani

Mohsen Ebrahimi Moghaddam

Shahid Beheshti University

https://doi.org/10.48308/jicse.2025.237528.1042

Abstract

With the rapid expansion of the Internet and social networks, the volume of images available online has increased dramatically, making it challenging for users to find relevant content. To address this problem, we propose incorporating emotion analysis as a key factor in understanding user preferences, thereby creating a more personalized and effective image recommendation system. In this article, we examine two approaches to utilizing emotional features in image recommendation. The first approach integrates emotional features directly into the feature vector used for training the recommendation model. The second approach refines recommendations through emotion-based postprocessing, where emotional proximity between users and images is used to re-rank recommendations. This study emphasizes the value of emotion analysis in advancing the personalization and efficacy of social image recommendation systems. Experimental results indicate that both approaches significantly improve recommendation performance, achieving higher metrics such as Recall@k and Precision@k. These findings demonstrate that emotional analysis enhances personalization and effectiveness in social image recommendation systems.

Keywords

Recommender system

emotion analysis

deep learning

social network

Subjects