Journal of Computer Applications (计算机应用) 2013/33:12 PP.3354-3358
Concerning the poor quality of recommendations of traditional user-based and item-based collaborative filtering models, a new collaborative filtering model combining users and items predictions was proposed. Firstly, it considered both users and items, and optimized the similarity model with excellent performance dynamically. Secondly, it constructed neighbor sets for the target objects by selecting some similar users and items according to the similarity values, and then obtained the user-based and item-based prediction results respectively based on some prediction functions. Finally, it gained final predictions by using the adaptive balance factor to coordinate both of the prediction results. Comparative experiments were carried out under different evaluation criteria, and the results show that, compared with some typical collaborative filtering models such as RSCF, HCFR and UNCF, the proposed model not only has better performance in prediction accuracy of items, but also does well in the precision and recall of recommendations.