Joint Topic-Semantic-aware Social Recommendation for Online Voting

TL;DR

This paper introduces a novel approach combining topic-enhanced word embeddings and social matrix factorization to improve online voting recommendations by effectively capturing content and social network structure.

Contribution

It proposes TEWE for better short text content representation and JTS-MF for integrating topic, semantics, and social information in voting recommendation.

Findings

TEWE outperforms traditional text embedding methods.

JTS-MF achieves higher recommendation accuracy than baselines.

The approach effectively leverages social network structure and voting content.

Abstract

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.