Abstract:A Web service is a method of communication between two electronic devices over a network. Web services have been widely employed for building service-oriented applications. Recommendation techniques are very important in the fields of E-commerce and other Web-based services. One of the main difficulties is dynamically providing highquality recommendation on sparse data. In this paper, a novel collaborative filtering-based Web service recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features.