A Hybrid Filtering for Micro-video Hashtag Recommendation using Graph-based Deep Neural Network

TL;DR

This paper introduces a hybrid filtering approach using graph-based deep neural networks to improve hashtag recommendations for micro-videos, addressing user relatedness and cold start issues effectively.

Contribution

It proposes a novel hybrid filtering method combining content and user-based collaborative filtering with graph neural networks for micro-video hashtag recommendation.

Findings

Achieves up to 6.5% improvement in F1 score on real datasets.

Effectively mitigates cold start user problem with a 15.8% F1 score increase.

Demonstrates superior performance over content-only methods.

Abstract

Due to the growing volume of user generated content, hashtags are employed as topic indicators to manage content efficiently on social media platforms. However, finding these vital topics is challenging in microvideos since they contain substantial information in a short duration. Existing methods that recommend hashtags for microvideos primarily focus on content and personalization while disregarding relatedness among users. Moreover, the cold start user issue prevails in hashtag recommendation systems. Considering the above, we propose a hybrid filtering based MIcro-video haSHtag recommendatiON MISHON technique to recommend hashtags for micro-videos. Besides content based filtering, we employ user-based collaborative filtering to enhance recommendations. Since hashtags reflect users topical interests, we find similar users based on historical tagging behavior to model user relatedness. We employ a graph-based deep neural network to model user to user, modality to modality, and user to modality interactions. We then use refined modality specific and user representations to recommend pertinent hashtags for microvideos. The empirical results on three real world datasets demonstrate that MISHON attains a comparative enhancement of 3.6, 2.8, and 6.5 reported in percentage concerning the F1 score, respectively. Since cold start users exist whose historical tagging information is unavailable, we also propose a content and social influence based technique to model the relatedness of cold start users with influential users. The proposed solution shows a relative improvement of 15.8 percent in the F1 score over its content only counterpart. These results show that the proposed framework mitigates the cold start user problem.