06100579

j

06100579 Zhao, Hong-Xia Wang, Xin-Hai Yang, Jiao-Ping Mixed collaborative recommendation algorithm based on factor analysis of user and item. J. Comput. Appl. 31, No. 5, 1382-1386 (2011). 2011 Science Press, Beijing ZH recommendation system collaborative filtering factor analysis

doi:10.3724/SP.J.1087.2011.01382

Summary: In order to solve the problems of data overload and data sparsity in the collaborative filtering recommendation (CFR) algorithm, factor analysis is adopted to reduce the dimension of the data and regression analysis is used to forecast the value that needs to be evaluated. Through these two methods, it not only reduces the amount of data but also maximizes the information retained. The ideas of the algorithm are as follows: first of all, the algorithm reduces the dimensions of user and item vector by factor analysis and some representative users and item factors can be obtained. Then, two regression models are established with target users and the evaluated items resp. the user factors and item factors as the independent variables. The final predictive value is achieved weighted by the two. By experimental simulation, the algorithm is demonstrated to be effective and feasible. Furthermore, the results show that the accuracy of the proposed algorithm is better than that of the collaborative filtering recommendation algorithm based on item.