Tag-Aware Recommender Systems: A State-of-the-art Survey

TL;DR

This survey reviews recent advances in tag-aware recommender systems, focusing on network, tensor, and topic-based approaches, highlighting progress, challenges, and future directions in leveraging social tagging data for personalized recommendations.

Contribution

It provides a comprehensive overview of state-of-the-art methods in tag-aware recommendation systems, categorizing approaches and identifying future research challenges.

Findings

Network-based methods effectively model user-item-tag relationships.

Tensor-based methods capture complex multi-dimensional data structures.

Topic-based methods uncover semantic relations among tags and items.

Abstract

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.