基于信息熵和用户行为一致性的协同过滤分组推荐

在仅以输入评分矩阵作为唯一算法输入的协同过滤推荐算法研究中,针对数据的质量不同带来的差异性对推荐结果的影响这一问题,包括对数据质量方面的重视与关注、如何刻画质量差异性以及如何针对不同质量数据的用户组别进行分组推荐建模等问题。提出针对数据质量的刻画,综合考虑用户行为一致性和用户信息熵两个指标对数据质量进行评价并对用户进行分组。对于不同组别的用户在分析其历史行为的基础上可以进行更精准的推荐建模。实验结果表明,数据质量的差异性确实对推荐精度的提升有着重要的影响,同时论证了对用户进行分组推荐的必要性。实验结果同时表明,运用用户行为一致性和用户信息熵两个指标的综合刻画带来的精度提升效果最为显著。

Collaborative filtering group recommendation based on information entropy and user behavior consistency

For the scoring matrix as the unique algorithm input of the collaborative filtering recommendation algorithm, the differences in the quality of the data have great impact on the recommendation results, including arousing the attention to data quality, how to characterize quality differences, and how to group users and recommend on the basis of user groups with different quality data. This paper proposed a description of data quality, comprehensively considered the user behavior consistency and user information entropy to evaluate the data quality. Users of different groups could perform more accurate recommendation results based on analyzing their historical behavior. The experimental results show that the difference of data quality has an important impact on the improvement of recommendation accuracy, and at the same time demonstrates the necessity of group recommendation. The experimental results also show that the accuracy of the combination of the two aspects of user behavior consistency and user information entropy is the most significant.