Design And Implement The Social Bookmark And Statistics Recommendation System Based On Web2.0

As one of the main application of web2.0, social bookmark essentially transfer user's personal collection from local into network. In this case, personal collection could be shared in network. For this reason social bookmark is also called network bookmark sometimes.People could use it to collect, categorize information which he's interested; meanwhile, social bookmark could also collect, analyze mass collection for analyzing, select special information and recommend them to others who might have the same or similar taste. In this way, information could be categorized into kinds of social knowledge, which could be found much more easily for specified kind of people. Social bookmark could make knowledge shared and communicated more easily.This paper derivates from the international study project"Statistical Physics of Information-An interdisciplinary Study of socio-economic systems", held by University of Fribourg, Switzerland and Renmin University of China. Its destination is designing and implementing a social bookmark system– Nextdoors, which could analyzes users'collection, and recommends correlative information for specified users. Firstly, the paper analyzes the relative knowledge, background and characteristics of Web2.0 social bookmark model, and points out from this model, system would be able to have useful users'information in order to form the basic data of calculation.Following, the paper takes J2EE as the application platform, it introduces Hibernate framework to visit database in ORM; it introduces DWR and JSON to make the system respond fast in Ajax manner; it introduces"Spring"framework to provide identical management for different kinds of components in system; it introduces Linux and Apache as the web container. Then, Nextdoors would be designed and implemented with all these technologies mentioned above.Finally, the paper analyzes how to describe a user voting prototype for specified domain in the data background process. Through the analysis of each inner properties and the weights of the users, the paper tries to select its recommendation and evaluate its accept degree for users. In this way, the paper aims to build a social bookmark system which could analyze precisely and recommend smartly.