Design And Implementation Of The College Entrance Examination Information Recommender System

With the development of the Internet, more and more accurate, specialization, personalized search requirement was need by people. How to let the user fast and accurate information that they need, is the key technology that the personalized search engine needs to solve. Recommended as a kind of information filtering system is an effective tools to solve information overload problem.The key to achieve recommend engine is accurately get user personal information or users browse information, then to acquiring information, then carry on correctly analysis in accurate description based on user features, with the most effective recommendation algorithm provides recommendation information. This paper mainly to recommend the core of the system technology, collaborative filtering technique are researched in this technology, Aiming at this technology, this paper cold start faults the analytical hierarchy process (ahp) and collaborative filtering algorithm was set up the way of the combination of the user model and filtering model, in this two kinds of model, the classification of users with different types of users and use different algorithms to undertake handling, finally to two model the corresponding experimental analysis and experimental results show that these two kinds of models in solving system on the issue of cold start-up obtain good effect.On the basis of the above, this paper designs and realizes the filtering algorithm based on the improvement of the university entrance exam information recommendation engine. Test results showed that the engine with high accuracy and user satisfaction, largely overcome low qualitythe problem of traditional recommendation engine recommended.