Harnessing the power of Social Bookmarking for improving tag-based Recommendations

TL;DR

This paper introduces a novel tag-based recommendation algorithm that clusters user tags semantically and combines them with user competency metrics, outperforming existing models in predicting user preferences.

Contribution

The paper presents an innovative algorithm that exclusively uses user tags and semantic clustering to improve personalized product recommendations.

Findings

Outperforms baseline Vector Space model in accuracy

Outperforms other state-of-the-art algorithms

Effective use of semantic similarity and user competency metrics

Abstract

Social bookmarking and tagging has emerged a new era in user collaboration. Collaborative Tagging allows users to annotate content of their liking, which via the appropriate algorithms can render useful for the provision of product recommendations. It is the case today for tag-based algorithms to work complementary to rating-based recommendation mechanisms to predict the user liking to various products. In this paper we propose an alternative algorithm for computing personalized recommendations of products, that uses exclusively the tags provided by the users. Our approach is based on the idea of using the semantic similarity of the user-provided tags for clustering them into groups of similar meaning. Afterwards, some measurable characteristics of users' Annotation Competency are combined with other metrics, such as user similarity, for computing predictions. The evaluation on data used from a real-world collaborative tagging system, citeUlike, confirmed that our approach outperforms the baseline Vector Space model, as well as other state of the art algorithms, predicting the user liking more accurately.