ALLEVIATING THE SPARSITY PROBLEM OF COLLABORATIVE FILTERING USING AN EFFICIENT ITERATIVE CLUSTERED PREDICTION TECHNIQUE
Collaborative filtering (CF) is one of the most prevalent recommendation techniques, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. Although CF has been widely applied in various applications, its applicability is restricted due to the data sparsity, the data inadequateness of new users and new items (cold start problem), and the growth of both the number of users and items in the database (scalability problem). In this paper, we propose an efficient iterative clustered prediction technique to transform user-item sparse matrix to a dense one and overcome the scalability problem. In this technique, spectral clustering algorithm is utilized to optimize the neighborhood selection and group the data into users' and items' clusters. Then, both clustered user-based and clustered item-based approaches are aggregated to efficiently predict the unknown ratings. Our experiments on MovieLens and book-crossing data sets indicate substantial and consistent improvements in recommendations accuracy compared to the hybrid user-based and item-based approach without clustering, hybrid approach with k-means and singular value decomposition (SVD)-based CF. Furthermore, we demonstrated the effectiveness of the proposed iterative technique and proved its performance through a varying number of iterations.
recommender system iterative-based collaborative filtering clustering-based collaborative filtering memory-based collaborative filtering