Mixed collaborative recommendation algorithm based onfactor analysis of user and item
Jiaoping Yang · Journal of Computer Applications · 2011
In order to solve the problems of data overload and data sparsity in Collaborative Filtering Recommendation(CFR) algorithm,the method of factor analysis was adopted to reduce the dimension of the data,and regression analysis was 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 use of factor analysis and some representative users and item factors could be got.And then,two regression models were established,with target users and the evaluated items as the dependent variables respectively,and the user factors and item factors as the independent variables respectively,which two predictive values of the evaluated items were achieved.Finally,the final predictive value was achieved weighted by the two.By experimental simulation,the algorithm is demonstrated effective and feasible.Furthermore,the results show that the accuracy of algorithm proposed here has somewhat increased compared with that of the collaborative filtering recommendation algorithm based on item.