Collaborative filtering based on iterative principal component analysis

Collaborative filtering (CF) is one of the most popular recommender system technologies, and utilizes the known preferences of a group of users to predict the unknown preference of a new user. However, the existing CF techniques has the drawback that it requires the entire existing data be maintained and analyzed repeatedly whenever new user ratings are added. To avoid such a problem, Eigentaste, a CF approach based on the principal component analysis (PCA), has been proposed. However, Eigentaste requires that each user rate every item in the so called gauge set for executing PCA, which may not be always feasible in practice. Developed in this article is an iterative PCA approach in which no gauge set is required, and singular value decomposition is employed for estimating missing ratings and dimensionality reduction. Principal component values for users in reduced dimension are used for clustering users. Then, the proposed approach is compared to Eigentaste in terms of the mean absolute error of prediction using the Jester, MovieLens, and EachMovie data sets. Experimental results show that the proposed approach, even without a gauge set, performs slightly better than Eigentaste regardless of the data set and clustering method employed, implying that it can be used as a useful alternative when defining a gauge set is neither possible nor practical.