Scalable Recommendations Using Clustering Based Collaborative Filtering

The development and growth in recommender systems address the issue of information overload faced by the online users while searching for products or services. However, recommender systems typically face challenges like data sparsity and scalability as they often handle large datasets. The most widely used recommendation technique is Collaborative Filtering (CF) that pins down the recommendations on the opinions of the most similar users. The core of a CF algorithm is the similarity computations among the users or items, which becomes extremely expensive when new users and items join the system at a very rapid rate. The proposed work deals with this scalability problem by implementing a clustering based CF approach. Typically in a recommendation problem there exists a set of users, a set of items and a rating matrix, that records the ratings assigned by the users to the items. In this work, we first partition the set of users using a CURE (Clustering using representatives) based method and then leverage the resultant clusters to formulate recommendations for the target user. In the proposed method, the CF algorithm is not applied to the entire user-item database, rather the algorithm is applied separately to each of the clusters resulting in reduced recommendation time. Moreover, Clustering also helps to improve the sparsity problem by reducing the dimension of the rating matrix and filtering out noisy data. The results of the experiments conducted on MovieLens-10M and MovieLens-20M datasets indicate that our method significantly reduces the runtime and at the same time preserves good recommendation quality.