为了验证用户对项目评分时所处的上下文环境是否会对用户的偏好产生影响,基于矩阵过程的马尔科夫分解方法,提出了一种基于上下文的流式推荐算法(streaming recommendation algorithm based on context,C-SRA),该方法可以从嘈杂的上下文中有效选取与评分相关的上下文信息,并将选取的上下文信息分为主观上下文和客观上下文两类。基于LDOS-CoMoDa数据集的两组对比实验显示,C-SRA算法无论是评分预测性能还是推荐性能均优于其他对比算法。
In order to verify whether the context environment in which users rate items would affect their preferences, according to the Markovian factorization of matrix processes method, a streaming recommendation algorithm based on context (C-SRA) is proposed. On the basis of this method, the selected context information is divided into subjective context and objective context, and the two types of context information and algorithm are integrated in turn. Finally, two groups of experiments based on LDOS-CoMoDa data set show that the C-SRA performed better than the other comparison algorithms in both rate prediction and the recommendation.
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