Yakut I., Polat H., KOÇ M.

International Conference on Knowledge Discovery and Information Retrieval (KDIR 2010), Valencia, Spain, 25 - 28 October 2010, pp.408-413 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • City: Valencia
  • Country: Spain
  • Page Numbers: pp.408-413
  • Keywords: Collaborative filtering, e-Commerce, projection, Scalability and Accuracy, PRINCIPAL COMPONENT ANALYSIS
  • Anadolu University Affiliated: Yes


Collaborative filtering (CF) systems are effective solutions for information overload problem while contributing web personalization. Different memory-based algorithms operating over entire data set have been utilized for CF purposes. However, they suffer from scalability, sparsity, and cold start problems. In this study, in order to overcome such problems, we propose a new approach based on projection matrix resulted from principal component analysis (PCA). We analyze the proposed scheme computationally; and show that it guarantees scalability while getting rid of sparsity and cold start problems. To evaluate the overall performance of the scheme, we perform experiments using two well-known real data sets. The results demonstrate that our scheme is able to provide accurate predictions efficiently. After analyzing the outcomes, we present some suggestions.