适应用户兴趣变化的改进型协同过滤算法

协同过滤算法可以根据用户的历史行为记录去预测其可能喜欢的物品，是现在业界应用极为广泛的推荐算法。但传统的协同过滤算法并没有考虑到用户兴趣的概念漂移，在一些基于时间的协同过滤算法中对推荐时效性的考虑也有所欠缺。针对这些问题，结合用户兴趣随时间转移的特点，改进了相似度的度量方法，同时引入一种增强的时间衰减模型来度量预测值，并将这两种方式有机地结合起来，解决了用户兴趣的概念漂移问题并考虑了推荐算法的时效性。仿真实验中，分别在不同的数据集中对比了该算法与UserCF、TCNCF、PTCF以及TimeSVD++算法的预测评分准确度和TopN推荐准确度。实验结果表明，改进算法能够降低预测评分的均方根误差（RMSE），并在TopN推荐准确度上均优于对比算法。

Improved adaptive collaborative filtering algorithm to change of user interest

As a widely used recommendation algorithm in the industry, collaborative filtering algorithm can predict the likely favorite items based on the user's historical behavior records. However, the traditional collaborative filtering algorithms do not take into account the drifting of user interests, and there are also some deficiencies when the recommendation's timeliness is considered. To solve these problems, the measure method of similarity was improved by combining with the characteristics of user interests change with time. At the same time, an enhanced time attenuation model was introduced to measure the predictive value. By combining these two ways together, the concept drifting problem of user interests was solved and the timeliness of the recommendation algorithm was also considered. In the simulation experiment, predictive scoring accuracy and TopN recommendation accuracy were compared among the proposed algorithm, UserCF, TCNCF, PTCF and TimesSVD++ algorithm in different data sets. The experimental results show that the improved algorithm can reduce the Root Mean Square Error (RMSE) of the prediction score, and it is better than all the compared algorithms on the accuracy of TopN recommendation.