【摘要】 【目的】通过挖掘用户与项目、用户与类别的关系特征,提取用户偏好,优化个性化推荐效果。【方法】提取用户对项目的评分和项目的度属性,挖掘用户偏好,提出用户–项目二部图上的游走条件;通过用户–项目–类别三部图映射到用户–类别二部图,构建类别–用户–项目–类别四部图;建立通过项目和类别共同挖掘用户偏好的个性化推荐方法。【结果】利用MovieLens电影评分数据,分别对基于二部图、加权二部图、三部图的方法与本文方法进行对比实验,结果表明,本文方法在准确率、MAE、召回率、覆盖率方面分别有所优化。【局限】MovieLens数据集缺少用户对电影评论性的文字数据集,不能通过语义分析用户偏好。【结论】本文对用户评分和项目度属性进行用户偏好分析,通过条件型游走四部图推荐方法,优化推荐效果。更多还原

【Abstract】 [Objective] By mining the relation characteristics between users and items, or between users and categories,this Paper extracts user preferences to optimize recommendation effect. [Methods] This paper extracts user rating and items degree attribute, mines user preferences, and puts forward the walk condition of User-Item bipartite graph; The category-User-Project-Category quadripartite graph is established by mapping User-Item-Category tripartite graph to the User-Category bipartite graph. The personalized recommendation method for user preferences through items and categories is proposed. [Results] Choosing MovieLens ratings data set as the source data, respectively comparing the experimental difference based on bipartite graph, weighted bipartite graph, tripartite graph and quadripartite graph, the results show that the Precision rate, MAE, recall rate, and coverage have been respectively optimized with this proposed method. [Limitations] Due to Movielens lack of critical textual data of users for movies, it is hard to analyze user preferences through the semantic. [Conclusions] This research analyzed user preferences through user ratings and degree attribute, it can be determined that the recommendation effect of quadripartite graph based on conditional walk is great.更多还原