User-Aware Folk Popularity Rank: User-Popularity-Based Tag Recommendation That Can Enhance Social Popularity

TL;DR

This paper introduces a user-aware tag recommendation method that improves social media post popularity by integrating user influence and tag usage tendencies into the existing FolkPopularityRank algorithm, leading to significantly increased views.

Contribution

The paper develops a novel algorithm that incorporates user popularity and tag tendencies into FP-Rank, enhancing social media post visibility beyond previous models.

Findings

Achieved 1.2 times more views in ten days compared to FP-Rank.

Validated with 60,000 training images and 1,000 test cases from a real SNS.

Demonstrated effectiveness in promoting individual and brand visibility.

Abstract

In this paper we propose a method that can enhance the social popularity of a post (i.e., the number of views or likes) by recommending appropriate hash tags considering both content popularity and user popularity. A previous approach called FolkPopularityRank (FP-Rank) considered only the relationship among images, tags, and their popularity. However, the popularity of an image/video is strongly affected by who uploaded it. Therefore, we develop an algorithm that can incorporate user popularity and users' tag usage tendency into the FP-Rank algorithm. The experimental results using 60,000 training images with their accompanying tags and 1,000 test data, which were actually uploaded to a real social network service (SNS), show that, in ten days, our proposed algorithm can achieve 1.2 times more views than the FP-Rank algorithm. This technology would be critical to individual users and companies/brands who want to promote themselves in SNSs.