Abstract

The development of social networking become a tremendous growth for providing relational information service to users based on various facts. Specifically, in location-based service (LBs) system gives the quality of service needs which the user wants to access. To know the user interest and behavior based service recommendation need advancement in personalized search. The major challenge in travel recommendation is the mutual relation of undesired service discovery from the huge amount of data in big data services. To improve the location-based service recommendation using a personalized log of information from social network suggestions. Develop a new data integrity management system to address the issue using the Personalized Location-based service selection based on quantitative self-analysis (PLBS2-QSA) model in personalized travel recommendation system. This system initially analyses the user log service to obtain the QoS service needs of user interest and personalized activity using quantitative self-analyses model (QSAM). To implicit the recommended logs based on the Point Of Interest (POI), the service is discovered. The service determined vector points finalize the ranked service using decision baseline classifier (DBLS) to the user to recommend the service. This recommendation system much improves the travel recommendation strategy by concentrates the quality of cost aware services producing higher performance service prediction system.

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