An empirical study of data smoothing methods for memory-based and hybrid collaborative filtering

Collaborative Filtering (CF) techniques are important in the e-business era as vital components of many recommender systems, for they facilitate the generation of high-quality recommendations by leveraging the similar preferences of community users. However, there is still a major problem preventing CF algorithms from achieving better effectiveness, the sparsity of training data. Lots of ratings in the training matrix are not collected. Few current CF methods try to do data smoothing before predicting the ratings of an active user. In this work, we have validated the effectiveness of data smoothing for memory-based and hybrid collaborative filtering algorithms. Our experiments show that all these algorithms achieve a higher accuracy after proper smoothing. The average mean absolute error improvements of the three CF algorithms, Item Based, k Nearest Neighbor and Personality Diagnosis, are 6.32%, 8.85% and 38.0% respectively. Moreover, we have compared different smoothing methods to show which works best for each of the algorithms.