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

Employing Spectral Domain Features for Efficient Collaborative Filtering