A Picture Tells a Thousand Words -- About You! User Interest Profiling from User Generated Visual Content

TL;DR

This paper introduces a novel method for inferring user interests on social networks by analyzing user-generated images, leveraging image similarity and categorization to improve personalization and recommendation systems.

Contribution

It presents a new approach that uses image content and organization to predict user interests, addressing the gap in visual content analysis in social network profiling.

Findings

Effective user interest inference from images on Pinterest

Improved personalization through visual content analysis

Demonstrated approach outperforms text-only methods

Abstract

Inference of online social network users' attributes and interests has been an active research topic. Accurate identification of users' attributes and interests is crucial for improving the performance of personalization and recommender systems. Most of the existing works have focused on textual content generated by the users and have successfully used it for predicting users' interests and other identifying attributes. However, little attention has been paid to user generated visual content (images) that is becoming increasingly popular and pervasive in recent times. We posit that images posted by users on online social networks are a reflection of topics they are interested in and propose an approach to infer user attributes from images posted by them. We analyze the content of individual images and then aggregate the image-level knowledge to infer user-level interest distribution. We employ image-level similarity to propagate the label information between images, as well as utilize the image category information derived from the user created organization structure to further propagate the category-level knowledge for all images. A real life social network dataset created from Pinterest is used for evaluation and the experimental results demonstrate the effectiveness of our proposed approach.