该文采用权重增量及相似聚集的用户行为分析算法，为用户推荐个性化视频提供了一个有效的解决方案。方法包含3个主要部分，首先利用RFM(Recentness, Frequency, Mon etary amount)模型分析用户的行为，将相同行为的用户归为一组；然后结合用户的最近习惯，使用基于权重增量的Apriori算法挖掘用户之间的关联规则，并用向量空间模型进行相似度计算从而实现用户相似聚集；最后进行协同过滤式推荐，完成整体个性化视频推荐过程。该方法的特点是行为数据自动收集获取，避免了直接对视频大数据的处理；另外，视频推荐随着用户行为的改变而动态变化，更加符合实际情况。实验结果表明，该方法有效并且稳定，相比于单一推荐方法，在准确率、召回率等综合指标上均有明显提升。%This paper presents an effective solution for personalized video recommendation based on the weight increment and similar aggregation user behavior analysis algorithm. The method is implemented in three steps:first, the user behavior is analyzed using the RFM (Recentness, Frequency, Monetary amount) model, users with the same behavior are classified as a group;second, the Apriori algorithm based on weight increment is applied to mining association rules between users in line with the recent habits of users, and by using the VSM model for similarity calculation, the user similarity aggregation is realized; finally, the whole process of personalized video recommendation is completed by means of collaborative filtering. The proposed method can automatically collects user behavioral data and avoids direct video big data processing. In addition, the video recommend dynamically changes with the change of user behavior. The experiment results show that, the presented effective and stable, and the method achieves significantly increasement in precision and recall comparing with the single recommendation method.
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