Authors:
Ibrahim Yakut
;
Huseyin Polat
and
Mehmet Koc
Affiliation:
Anadolu University, Turkey
Keyword(s):
Collaborative filtering, e-Commerce, projection, Scalability and Accuracy.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining in Electronic Commerce
;
Data Reduction and Quality Assessment
;
Interactive and Online Data Mining
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
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.