社交网络中上下文感知协同过滤算法

摘要

围绕上下文感知推荐技术和社会化网络推荐技术的局限性展开研究,提出一种基于社会化网络环境下的名为HCCF的上下文感知协同过滤方法.在充分考虑上下文感知推荐系统实际问题的基础上,首先量化了不同维度的上下文对推荐系统所产生的影响,并在此基础上定义了上下文影响系数.在此基础上引入了社会化网络环境中不同用户之间的相互影响,并采用社会化网络用户信任度进行衡量,最后对上下文因素和社会化网络用户信任度进行综合考虑,提出一种新的相似度计算方法.理论分析和在真实数据集上的实验结果表明,相对于单纯基于上下文的系统过滤算法以及社会化网络推荐方法而言,该算法的准确性和推荐效率均得到一定程度的提升.%A context-aware collaborative filtering method named HCCF based on social network environment is proposed, which focuses on the limitations of context-aware recommendation technology and social network recommendation technology.On the basis of fully considering the practical problems of the context-aware recommendation system, the influence of different dimensions of the context on the recommendation system is quantified, and the context influence coefficient is defined on this basis.And then introduces the interaction between different users in the social network environment, and uses the social network user trust degree to measure.Finally, a new similarity calculation method is proposed considering the context factor and the social network user trust.Theoretical analysis and experimental results on the real data set show that the proposed algorithm improves the accuracy and recommendation efficiency compared with the simple context-based system filtering algorithm and the social network recommendation method.