Abstract: 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.

Key words: recommendation system, collaborative filtering, factor analysis

摘要： 为解决协同过滤推荐(CFR)算法中的数据量过大和数据稀疏性的问题,采用因子分析的方法对数据降维,并使用回归分析方法预测待评估值,既减少了数据量又最大限度保留了信息。该算法首先,采用因子分析的方法将用户和项目降维为若干用户因子和若干项目因子;然后,以目标用户为因变量,以用户因子为自变量建立一个回归模型,并且以待评价项目为因变量,以项目因子为自变量建立另一个回归模型,进而得到目标用户在待评项目上的两个预测值;最后,通过两者的加权得到最终的预测值。实验仿真证实了算法的可行性和有效性。实验结果表明,该算法比基于项目的协同过滤推荐算法在精确度上有所提高。

关键词: 推荐系统, 协同过滤, 因子分析