What You Like: Generating Explainable Topical Recommendations for Twitter Using Social Annotations

TL;DR

This paper introduces an explainable topical recommendation system for Twitter that leverages social annotations to help users discover relevant content, overcoming challenges like lack of ratings and passive user behavior.

Contribution

The paper proposes a novel topical recommendation approach using social annotations, providing explainability and addressing issues of rating scarcity and passive users in social networks.

Findings

Outperforms state-of-the-art collaborative filtering methods.

Provides effective topical explanations for recommendations.

Improves user acceptance of recommendations.

Abstract

With over 500 million tweets posted per day, in Twitter, it is difficult for Twitter users to discover interesting content from the deluge of uninteresting posts. In this work, we present a novel, explainable, topical recommendation system, that utilizes social annotations, to help Twitter users discover tweets, on topics of their interest. A major challenge in using traditional rating dependent recommendation systems, like collaborative filtering and content based systems, in high volume social networks is that, due to attention scarcity most items do not get any ratings. Additionally, the fact that most Twitter users are passive consumers, with 44% users never tweeting, makes it very difficult to use user ratings for generating recommendations. Further, a key challenge in developing recommendation systems is that in many cases users reject relevant recommendations if they are totally unfamiliar with the recommended item. Providing a suitable explanation, for why the item is recommended, significantly improves the acceptability of recommendation. By virtue of being a topical recommendation system our method is able to present simple topical explanations for the generated recommendations. Comparisons with state-of-the-art matrix factorization based collaborative filtering, content based and social recommendations demonstrate the efficacy of the proposed approach.