Research And Implementation Of Recommendation System With Distributed Data Sources

With the popularity and development of the Internet, the way people accessinformation are constantly changing. This change has presented new technicalchallenges for service providers. The general trend is that people start to prefer theway servers “push” information to them over the way they “pull” information fromthe servers. With such a large amount of information, people want to get apersonalized service.Personalized recommendation is center of all personalized service. In recentyears, it has been successfully applied to fields like e-commerce, movies and musiccommunities. Currently, research of recommendation algorithms is mainly focusedon the content-based recommendation, collaborative filtering recommendation andrule-based recommendation. They all have their own advantages and disadvantagestherefore suitable for different applications. Publish/subscribe system also plays animportant part as a personalized service. Server push is especially important inmobile Internet applications. Major companies such as Apple, Google haveimplemented their own push service. In the Internet environment, information isoften distributed. It s a new attempt to implement personalized recommendationservice in such environment. This paper explores the design and implementation ofpersonalized recommendations in a distributed environment.The main idea of this paper is to design the recommendation algorithm andpublish/subscribe system for the local data sources, and then proposed anSOA-based system architecture to incooperate the distributed data sources. First,based on the TF-IDF vector model for documents, we design a hybridrecommendation algorithm of content-based recommendation and rule-basedrecommendation and optimize them by means like kd-tree and domain descriptionlanguage. We also introduce a feedback mechanism to learn the users interests.Next a topic tree based matching algorithm used to implement a publish/subscribesystem. And a separate server push service is implemented to handle multiple pushclients. Finally, we provide personalized recommendation services in a distributedenvironment by utilizing the SOA design ideas, encapsulating the functions ofvarious data sources to provide a service as a whole. This achieves interoperabilityamong all data sources and efficient sharing of information.