Employing Spectral Domain Features for Efficient Collaborative Filtering

TL;DR

This paper introduces a spectral domain feature-based collaborative filtering method that improves efficiency, scalability, and accuracy by using frequency domain analysis of user ratings with clustering and a novel similarity measure.

Contribution

It combines self-organizing maps, k-means clustering, and DFT-based spectral analysis to enhance collaborative filtering performance and address scalability and sparsity issues.

Findings

Outperforms existing similarity measures in accuracy.

Reduces computational time compared to state-of-the-art methods.

Effectively handles sparse data with spectral features.

Abstract

Collaborative filtering (CF) is a powerful recommender system that generates a list of recommended items for an active user based on the ratings of similar users. This paper presents a novel approach to CF by first finding the set of users similar to the active user by adopting self-organizing maps (SOM), followed by k-means clustering. Then, the ratings for each item in the cluster closest to the active user are mapped to the frequency domain using the Discrete Fourier Transform (DFT). The power spectra of the mapped ratings are generated, and a new similarity measure based on the coherence of these power spectra is calculated. The proposed similarity measure is more time efficient than current state-of-the-art measures. Moreover, it can capture the global similarity between the profiles of users. Experimental results show that the proposed approach overcomes the major problems in existing CF algorithms as follows: First, it mitigates the scalability problem by creating clusters of similar users and applying the time-efficient similarity measure. Second, its frequency-based similarity measure is less sensitive to sparsity problems because the DFT performs efficiently even with sparse data. Third, it outperforms standard similarity measures in terms of accuracy.