Combinations of Jaccard with Numerical Measures for Collaborative Filtering Enhancement: Current Work and Future Proposal

TL;DR

This paper proposes combining Jaccard similarity with numerical measures like cosine and Pearson to enhance collaborative filtering, demonstrating improved recommendation accuracy on the MovieLens dataset.

Contribution

It introduces novel combined similarity measures that leverage both existence and magnitude information, outperforming individual measures in collaborative filtering.

Findings

Combined measures outperform single similarity measures in experiments.

Experimental results show significant improvement on MovieLens dataset.

Combined measures enhance recommendation accuracy across evaluation metrics.

Abstract

Collaborative filtering (CF) is an important approach for recommendation system which is widely used in a great number of aspects of our life, heavily in the online-based commercial systems. One popular algorithms in CF is the K-nearest neighbors (KNN) algorithm, in which the similarity measures are used to determine nearest neighbors of a user, and thus to quantify the dependency degree between the relative user/item pair. Consequently, CF approach is not just sensitive to the similarity measure, yet it is completely contingent on selection of that measure. While Jaccard - as one of those commonly used similarity measures for CF tasks - concerns the existence of ratings, other numerical measures such as cosine and Pearson concern the magnitude of ratings. Particularly speaking, Jaccard is not a dominant measure, but it is long proven to be an important factor to improve any measure. Therefore, in our continuous efforts to find the most effective similarity measures for CF, this research focuses on proposing new similarity measure via combining Jaccard with several numerical measures. The combined measures would take the advantages of both existence and magnitude. Experimental results on, Movie-lens dataset, showed that the combined measures are preeminent outperforming all single measures over the considered evaluation metrics.