Model For Personalized Resources Recommendation Based On Tags Clustering

With the rapid development of the Internet, the phenomenon of information overloaded occurred, users were drowning in mass information, but they can’t find valuable and interesting information what they need quickly. Therefore it is necessary to recommend contents and information to meet users’ different needs those have different backgrounds, different needs and different hobbies. Then personalized recommendation system will come into being, and is considered to be an important means to resolve the information overload in current. In social tagging system, which is one of the main application of web2.0,tags which was used to label resources by users reflects both the user’s own interest, but also reflects the characteristics of the resources, This makes it very suitable to recommend personalized resources for users to meet their individual needs.Firstly, this paper analyzes the research status about personalized recommendation and how to use social tags to personalized recommendation in overseas and domestic, summarized the relevant theory, and analyzes the existing problems in current researches. Then put forward a personalized resource recommendation model based on social tagging clustering. Constructed the co-occurrence network between tags, and then use the algorithm of AL which is used for finding communities in complex networks to cluster social tags in order to solve semantic fuzziness of social tags. Modeling user interests according to the frequency and the time of tags the user to label resources to reflect the dynamic changes of user interest. Finally, complete the personalized recommendation based on user-based collaborative filtering recommendation. And verify the validity of the proposed model through experiments.