Employing Spectral Domain Features for Efficient Collaborative Filtering
This addresses efficiency and accuracy issues in recommender systems for users, though it appears incremental as it builds on existing clustering and spectral methods.
The paper tackles collaborative filtering's scalability and sparsity problems by proposing a frequency-domain similarity measure based on DFT power spectra coherence, which is more time-efficient and achieves higher accuracy than standard measures.
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.