A Random Walk Model for Item Recommendation in Folksonomies

TL;DR

This paper introduces a random walk algorithm that leverages social tagging data to improve item recommendation, effectively addressing data sparsity issues in folksonomies.

Contribution

It proposes a novel random walk approach with smoothing strategies to enhance recommendations in sparse social tagging environments.

Findings

The algorithm outperforms traditional methods on real-world datasets.

Smoothing strategies improve recommendation accuracy.

The approach effectively captures transitive user-item-tag relationships.

Abstract

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web2.0 applications. The tags provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of ternary <user, tag, item> interaction data limits the performance of tag-based collaborative filtering. This paper proposes a random-walk-based algorithm to deal with the sparsity problem in social tagging data, which captures the potential transitive associations between users and items through their interaction with tags. In particular, two smoothing strategies are presented from both the user-centric and item-centric perspectives. Experiments on real-world data sets empirically demonstrate the efficacy of the proposed algorithm.