关键词: 综合云模型, 属性评价, 评分聚类, 属性聚类, 协同过滤

Abstract: In order to solve the problem of lack of co-rated users caused by data sparseness and similarity calculation method, the authors, by making use of the advantage of cloud model transformation between qualitative concept and quantitative numerical value, proposed an improved personal recommendation algorithm based on cloud model and users clustering. The users’ preference on the evaluation of item attribute was transformed to preference on digital characteristics represented by integrated cloud model. By using the improved clustering algorithm, the authors clustered the rating data and the standardized original user attribute information, and at the same time, by taking into account the changes of the users’ interests, recommended the neighbor users’ union generated by similarity based on integrated cloud model of items attributes evaluation between users, clustering of users for item rating, and clustering of user attributes these three methods. The theoretical analysis and experimental results show that the proposed improved algorithm can not only solve the problem of lack of co-rated users caused by data sparseness, but also obtain satisfactory mean absolute error and root-mean-square error even when the users are new. Theoretical analysis and experimental results show that the proposed improved algorithm can not only solve the problem of lack of co-rated users caused by sparseness data, but also obtain satisfactory mean absolute error and root-mean-square error even when the users are new.

Key words: integrated cloud model, attributes evaluation, rating clustering, attribute clustering, collaborative filtering