With the rapid development of service-oriented computing technologies, large amounts of Web services have been released on the Internet to facilitate system construction. Consequently, personalized service recommenders are emerging to handle information overload in service computing. Collaborative filtering (CF) is popular for service recommendations based on explicit ratings or Quality of Service (QoS) information provided by users. In real cases, however, it is difficult to collect explicit feedbacks since most available feedbacks are implicit. In this paper, the authors propose an implicit-to-explicit rating approach, which can leverage the implicit feedback from user's collecting behavior to build a CF-based recommender system for Web services. Firstly, a user-service binary matrix is constructed based on the collection records in the watchlist. Secondly, the reputation rating, publishing time and tag information of services are combined into the previous binary matrix to generate a pseudo rating matrix, which can reflect users' preference changing more precisely. Thirdly, both the traditional and enhanced user-based CF methods are used to generate a personalized service list with these pseudo ratings. Finally, a set of experiments are designed to validate the proposed service recommendation approach based on a large scale and real-world dataset from a wellknown service registry center Programmableweb (PWeb). The experimental results show that our hybrid recommendation method is more efficient and accurate than the traditional log-based CF methods.