Software service recommendation base on collaborative filtering neural network model

Abstract

With broad application of Web service technology, many users look for applicable Web services to construct their target application quickly or do some further research. Github, as a treasury including a variety of software programs, provides functional code modules for those in need, which has become their characteristic service. However, tremendous Web services have been developed all the time which increase the difficulty to find the target or interested services for users. Service recommendation has become of practical importance. There is few studies in the personalized repository recommendation of Github. In this paper, we present a general framework of PNCF, a preference-based neural collaborative filtering recommender model, and develop the instantiation of PNCF framework in Github repository recommendation with language preference called LR-PNCF. We use a neural network to capture the non-linear user-repository relationships and obtain abstract data representation from sparse vectors. Comprehensive experiments conducted on a real world dataset demonstrate the effectiveness of the proposed approach.