Social Explorative Attention based Recommendation for Content Distribution Platforms

TL;DR

This paper introduces SEAN, a social recommendation framework that personalizes content suggestions by leveraging user interests and social network data, improving accuracy and diversity on social media platforms.

Contribution

The paper proposes SEAN, a novel social recommendation model with two versions that incorporate user interests and social connections, outperforming existing methods in accuracy and diversity.

Findings

SEAN outperforms state-of-the-art methods in accuracy.

SEAN improves recommendation diversity and equality.

SEAN is effective across multiple languages and datasets.

Abstract

In modern social media platforms, an effective content recommendation should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. To address the limitations of existing methods for social recommendation, we propose Social Explorative Attention Network (SEAN), a social recommendation framework that uses a personalized content recommendation model to encourage personal interests driven recommendation. SEAN has two versions: (1) SEAN-END2END allows user's attention vector to attend their personalized interested points in the documents. (2) SEAN-KEYWORD extracts keywords from users' historical readings to capture their long-term interests. It is much faster than the first version, more suitable for practical usage, while SEAN-END2END is more effective. Both versions allow the personalization factors to attend to users' higher-order friends on the social network to improve the accuracy and diversity of recommendation results. Constructing two datasets in two languages, English and Spanish, from a popular decentralized content distribution platform, Steemit, we compare SEAN models with state-of-the-art collaborative filtering (CF) and content based recommendation approaches. Experimental results demonstrate the effectiveness of SEAN in terms of both Gini coefficients for recommendation equality and F1 scores for recommendation accuracy.