Pinterest Board Recommendation for Twitter Users

TL;DR

This paper introduces a new pinboard recommendation system for Twitter users that uses multi-label classification and visual diversification to connect Twitter interests with Pinterest content, validated on a dataset of 2000 users.

Contribution

The paper presents a novel approach combining multi-label classification and visual diversification for cross-platform pinboard recommendations.

Findings

Validated system on 2000 users dataset

Effective association of Twitter interests with Pinterest pinboards

Potential for improved social media content discovery

Abstract

Pinboard on Pinterest is an emerging media to engage online social media users, on which users post online images for specific topics. Regardless of its significance, there is little previous work specifically to facilitate information discovery based on pinboards. This paper proposes a novel pinboard recommendation system for Twitter users. In order to associate contents from the two social media platforms, we propose to use MultiLabel classification to map Twitter user followees to pinboard topics and visual diversification to recommend pinboards given user interested topics. A preliminary experiment on a dataset with 2000 users validated our proposed system.