Using Wikipedia to Boost SVD Recommender Systems

TL;DR

This paper enhances SVD recommender systems by integrating Wikipedia data through item mapping and similarity measures, significantly improving accuracy in sparse data scenarios and allowing easy integration with existing models.

Contribution

It introduces a method to incorporate Wikipedia information into SVD-based recommenders, improving performance especially with sparse datasets.

Findings

Improved recommendation accuracy on MovieLens dataset

Effective integration of Wikipedia data into SVD models

Enhanced performance in high-sparsity conditions

Abstract

Singular Value Decomposition (SVD) has been used successfully in recent years in the area of recommender systems. In this paper we present how this model can be extended to consider both user ratings and information from Wikipedia. By mapping items to Wikipedia pages and quantifying their similarity, we are able to use this information in order to improve recommendation accuracy, especially when the sparsity is high. Another advantage of the proposed approach is the fact that it can be easily integrated into any other SVD implementation, regardless of additional parameters that may have been added to it. Preliminary experimental results on the MovieLens dataset are encouraging.