  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.
