Locality Sensitive Hashing Based Scalable Collaborative Filtering

Neighborhood-based collaborative filtering methods are widely used in recommender systems because of their easy-to-implement and effective nature. One important drawback of these methods is that they do not scale well with increasing amounts of data. In this work we applied the locality sensitive hashing technique for solving the scalability problem of neighborhood-based collaborative filtering. We evaluate the effects of the parameters of locality sensitive hashing technique on the scalability and the accuracy of the developed recommender system.