基于用户分类的协同过滤个性化推荐方法研究

Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification

【目的】解决随着用户数目剧增而造成的协同过滤算法效率过低的问题。【方法】提出一种基于用户分类的协同过滤方法。该方法引入基于规则的分类方法对庞大的用户群分类,在保证一定的推荐准确度前提下,为用户寻找局部近邻用户,并以局部近邻用户基准完成个性化推荐。【结果】分别通过F1与平均绝对误差两个指标进行用户分类与推荐精度评估,在用户分类准确及推荐精度良好的前提下,用时间复杂度衡量算法效率。实验结果表明,引入用户分类的协同过滤推荐效率明显提高。【局限】牺牲一定程度的推荐精度;仅在Movie Lens公开数据集上进行实验测试,还需在其他数据集上进一步检验。【结论】本文方法可以减少近邻用户识别的计算量,同时提高算法效率。

[Objective] To solve the problem of low efficiency of the algorithm with the increasing number of users. [Methods] This paper proposes a method of collaborative filtering based on user classification. Firstly, the huge users are classified into several groups according to a rule-based classification method. Then, with the guarantee of recommendation accuracy, the local neighbor users are discovered for users. Finally, based on the discovered local neighbors, personalized recommendation is conducted. [Results] User classification and recommendation accuracy are evaluated by F~ and MAE separately. The algorithm efficiency is evaluated according to the time complexity. Experimental results show that with the adoption of a rule-based user classification, collaborative filtering algorithm significantly improves with the guarantee of user classification accuracy and recommendation accuracy. [Limitations] The recommendation accuracy is reduced a little bit. The proposed method is only tested on MovieLens data set, and it needs further validation in other data sets. [Conclusions] This method reduces the computation of local neighbors user identification, while improves the efficiency of the algorithm.