基于情境感知的个性化推荐算法

摘要

With the rapid development of Internet,the traditional personalized recommendation involving only users and projects cannot meet demands in efficiency and accuracy of the recommendation.Therefore,context-aware recommendation has drawn wide attention and become a new research hotspot.This paper analyzes the definition of context and the model of context-aware recommendation.It also proposes an association rule recommendation model based on context information which reduces the number of dimensions.The data source of experiments is web log.Finally,this paper combines the temporal context and implements the association rule recommendation algorithm based on the temporal context partition.Compared with the traditional recommendation algorithm,the results of experiments show that the context-aware recommendation algorithm has higher accuracy and recall rate.%随着互联网的快速发展,只涉及用户和项目的传统个性化推荐已不能满足推荐要求的效率和准确率.因此,情景感知个性化推荐服务引起了广泛关注,成为新的研究热点.本文分析了情境的定义、情景感知个性化推荐模型,并提出了一种基于情境信息降低维度的关联规则推荐模型.最后,以视频网站的web日志为数据源,融合时间情境因素,实现了基于时间情境划分的关联规则推荐算法,并和传统推荐算法进行对比分析,实验证明,情境感知推荐算法具有更高的准确率和召回率.