关键词: 多服务价值链, 业务资源推荐, 聚类算法, 惩罚因子, 元学习

Abstract: There are many enterprise and business resources in the multi-service value chain.Through recommendation technology,high-quality business resources are recommended to different enterprise users to help enterprises reduce costs and increase value.But the traditional recommendation technology has the problems of business resource reduction and the long tail effect,which seriously affects the recommendation quality.To solve the above problems,a recommendation algorithm of business resources for multi-service value chain was proposed.The algorithm learned the correspondence between the optimal clustering algorithm and the meta-attributes of the data set through meta learning to select the optimal clustering algorithm for the data set at a certain time and convert the original high-dimensional scoring matrix of business resources into sub-scoring matrices of multiple low-dimensional.In the sub-scoring matrix,the penalty factor optimization collaborative filtering was used to alleviate the long tail problem of business resource recommendation.Though experimental results confirmed,the effectiveness of the proposed algorithm was proved.Compared with other recommended algorithms,the proposed algorithm had significantly improved in mean absolute error,root mean square error,comprehensive evaluation index and coverage.

Key words: multi-service value chain, business resource recommendation, clustering algorithm, penalty factor, meta-learning