Collaborative filtering (CF) is one of the most efficient techniques to produce personalized recommendations and to deal with the information overload of modern times. Although CF techniques have immensely useful filtering capabilities, many CF systems have challenging problems like scalability, accuracy, and privacy. One approach to enhance scalability of such systems is to apply discrete wavelet transformation (DWT) techniques. DWT-based CF schemes significantly overcome the scalability problem. However, they fail to protect individual users' privacy. Moreover, although such schemes provide accurate predictions, the quality of the recommendations provided by DWT-based CF schemes can be further improved by applying some preprocessing methods.