Web Service Recommendation Based on Watchlist via Temporal and Tag Preference Fusion

With the increasing number of Web services available on the Internet, how to recommend Web services to interested users effectively and efficiently remains to be a big challenge. At present, collaborative filtering (CF) is the most widely used technique in the design of recommender systems to handle information overload. For Web services, however, it is difficult for user to collect personalized QoS (Quality of Service)data and other explicit feedbacks such as ratings. In most cases, only a part of the implicit feedbacks (e.g., watchlist) is available in service registry. In this paper, we leverage implicit feedback from user's watchlist to build a CF-based recommender system for Web service. Our main contribution is to transform implicit feedbacks into explicit ratings to improve the accuracy of service recommendation. More specifically, we first construct binary user-service rating matrix according to the implicit feedback from the watchlist. Then, temporal and tag preference are combined into the original rating matrix to generate a more accurate pseudo rating matrix, which can reflect users' different preference on services in their own watchlists. Finally, we use traditional user-based CF method to produce a personalized service recommendation list with corresponding pseudo ratings. Moreover, the empirical experiments based on ProgrammableWeb show that compared with traditional log-based CF method, the recommender system with temporal and tag preference is more accurate and precise.