Unified collaborative filtering model based on combination of latent features

Authors:
Jiang Zhong;Xue Li
Affiliations:
College of Computer Science, Chongqing University, Chongqing 400044, China;School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Collaborative filtering (CF) has been studied extensively in the literature and is demonstrated successfully in many different types of personalized recommender systems. In this paper, we propose a unified method combining the latent and external features of users and items for accurate recommendation. A mapping scheme for collaborative filtering problem to text analysis problem is introduced, and the probabilistic latent semantic analysis was used to calculate the latent features based on the historical rating data. The main advantages of this technique over standard memory-based methods are the higher accuracy, constant time prediction, and an explicit and compact model representation. The experimental evaluation shows that substantial improvements in accuracy over existing methods can be obtained.