Open AccessCollaborative Filtering Method of Web Service Recommendation Based on Location Aware and Personalized
Author(s) -
Li Ziman
Publication year - 2021
Publication title -
converter
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.104
H-Index - 1
ISSN - 0010-8189
DOI - 10.17762/converter.233
Subject(s) - computer science , collaborative filtering , web service , cluster analysis , quality of service , service (business) , key (lock) , recommender system , data mining , similarity (geometry) , ws policy , database , world wide web , web application security , computer network , web development , machine learning , computer security , artificial intelligence , economy , economics , image (mathematics)
With the rapid growth of the number of Web services, it is necessary to build an efficient web service recommendation system in the face of massive web services. In order to recommend high-quality services to users, the key problem is how to obtain the s value of Web services. This paper proposes a collaborative web service recommendation method based on location clustering. Firstly, users are clustered according to the autonomous system by using the correlation between QoS and user location. According to the clustering results, the system fills in the vacancy Qos value; Then, the vacancy Qos value is filled in in advance and the similarity between active users and each user is calculated. Based on this, to P-K algorithm is used to obtain the most similar Qos value to predict the unknown service for active users to complete the recommendation. The method proposed in this paper can effectively solve the problem of data sparsity and cold start of Web services. At the same time, a better balance between accuracy and coverage is obtained.