Collaborative Filtering Method of Web Service Recommendation Based on Content Awareness and Personalization

Abstract

The rapid development of web has brought the status quo of information explosion. Now the research of personalized web service recommendation information system has become a hot research direction in the field of service computing. The research of web service recommendation system mainly solves two problems: prediction and completion of sparse QoS data, user personalized recommendation. Firstly, this paper proposes an improved collaborative filtering web service recommendation algorithm based on user preferences. Secondly, based on the improved collaborative filtering web service recommendation algorithm based on user preference (UPCF), this paper proposes an improved collaborative filtering web service recommendation algorithm based on joint user preference (CUPCF). The algorithm extracts user preference data from QoS data and uses it to select similar neighbors. After the Top-k algorithm is used to determine the set of similar neighbors of target users and services, the QoS data is used to calculate the similarity of neighbors. Finally, based on CUPCF algorithm, this paper proposes an improved collaborative filtering web service recommendation algorithm based on user location and preference. Experimental data show that the algorithm can improve the efficiency of recommendation and filtering.