Abstract:  In order to enable enterprises to quickly obtain the required digital media resources from the massive data resources in the multi service value chain, a digital media resource recommendation algorithm for the multi service value chain is designed. A dataset of digital media resources is established, the concept of meta learning is integrated, optimal clustering method is used to partition the attributes of digital media resources in different time periods, the probability of users being divided into the same cluster through consistency matrix is calculated, and a multi service value chain digital resource recommendation objective function is constructed under phase space reconstruction. The potential Dirichlet allocation method is introduced to solve the objective function, and penalty factors are used to handle the long tail problem of digital media resource recommendation. Resources are arranged according to user interest levels to achieve high-precision digital media resource recommendation. The experimental results show that the proposed algorithm has high accuracy in user preference evaluation and recommendation coverage, effectively improving the quality of digital media resource recommendations and bringing new opportunities for the healthy development of enterprises. 

Key words: resource recommendation algorithm, information clustering, coverage rate, multi service value chain, digital media