Location-aware and Personalized Collaborative Filtering for Web Service Recommendation
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Abstract
As the number of web services with similar functionality increases, the service users usually depend on web recommendation systems. Now a days the service users pay more importance on nonfunctional properties which are also known as Quality of Service (QoS) while finding and selecting appropriate web services. Collaborative filtering approach predicts the QoS values of the web services effectively. Existing recommendation systems rarely consider the personalized influence of the users and services in determining the similarity between users and services. The proposed system is a ranking oriented hybrid approach which integrates user-based and item-based QoS predictions. Many of the non-functional properties depend on the user and the service location. The system thus employs the location information of users and services in selecting similar neighbors for the target user and service and thereby making personalized service recommendation for service users. General Terms Service computing, Recommendation
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