Time-aware Service Recommendation Algorithm Based on K-means and Low-rank Matrix Factorization

In recent years, with the emergence of more and more similar Web services, how to recommend high-quality Web services to consumers has become a challenging task. Traditional service recommendation has the problems of cold start and data sparseness, resulting in low accuracy of service recommendation through similar users of the target user. In response to the above problems, this paper proposes a time-aware service recommendation algorithm based on K-means and low-rank matrix factorization (K-LMF). First, this paper uses the K-means clustering algorithm to gather data with similar characteristics into the same cluster to improve the efficiency and accuracy of service recommendation. In order to prevent data sparseness from causing unpredictability, this paper divides the data from multiple dimensions, and then uses the low-rank matrix decomposition algorithm of the L1 paradigm to complete the quality of service (QoS) matrix to predict the appropriate service for the target user. Finally, experiments are carried out on the WS-DREAM data set to verify the effectiveness and feasibility of this scheme.