考虑负相关性信息的协同过滤算法研究

为了研究Pearson负相关性信息对协同过滤算法的影响, 提出了一种考虑负相关性信息的协同过滤算法。该算法选取正相关用户作为最近邻居, 负相关用户作为最远邻居, 使用参数调节最近邻居和最远邻居在推荐过程中的作用。MovieLens数据集上的对比实验表明, 负相关性不仅可以提高推荐结果的准确性, 而且可以增加推荐列表的多样性; 进一步分析发现, 负相关性还可以大幅度提高不活跃用户的推荐准确性。该工作表明, 负相关性有助于解决推荐系统中准确性、多样性两难的问题和冷启动问题。

Collaborative filtering algorithm by considering negative correlations

In order to study the effect of the negative correlation of Pearson to collaborative filtering algorithm, this paper presented an improved collaborative filtering algorithm. Firstly this algorithm selected the positive and negative correlation users as the nearest neighbor set and furthest neighbor set respectively, and then made use of a tunable parameter to adjust the effect of the nearest and furthest neighbor set on recommendation. The experiment results on MovieLens dataset show that negative correlations can not only significantly improve the accuracy of recommendations, but also increase the diversity of recommendation lists. It has found that collaborative filtering algorithm by considering the negative correlations can greatly improve the recommendation accuracy of users with small degrees. This work suggests that the negative correlations help solve the dilemma of accuracy and diversity and cold start problem of the recommender systems.