Abstract: With the rapid growth of the number of web services,the problem of service overload has gradually emerged.To relieve service overload,and help users position high-quality services rapidly,service recommendation has become a hot research topic in the field of service computing.Aiming at the difficulties of cold start and data sparseness in current service recommendation,this paper proposes a quality of service(QoS) prediction recommendation algorithm SRMFC based on the multi-features crossing,which implements multi-features through the “word embedding” method to improve the performance of the algorithm in dealing with the cold start.At the same time,a neural network is used to complete the automatic cross of multi-features.Compared with traditional collaborative filtering,factorization machine and other methods,the proposed algorithm can achieve in-depth exploration of the relationship between features,and improve the learning ability of the algorithm in dealing with extremely sparse data scenarios.Experiments on public data sets show that,under different data sparsity scenarios,the service quality prediction error of the SRMCF intersection decrease by at least 20% compared with the mainstream service recommendation algorithm in recent years.

Key words: Service overload, Cold start, Data sparseness, QoS prediction, Service recommendation