Research On Collaborative Filtering In Personality Recommendation Systems

Along with the rapid development of Internet and information technology, personalization recommendation has become one method of the new intelligent service. According to the analysis of consumers' individuality, habit and favor, the system provides information and service to the consumer which they want. Consequently, the problem of "information overloading" and "information maze" has been solved. Today, more and more researchers have focused on this field.There are many ways to actualize personalization recommendation. The most popular and effective one is Collaborative Filtering, including User-based and Item-based recommendation arithmetic. However, the efficiency of this technology decline by the increasing number of users and items, which results to extremely sparse data of users' assessments and other problems. Therefore the traditional arithmetic need improve.The major contributions of the thesis are as follows:(1) This thesis study deeply on personalization recommendation system, including its application status, input and output format, category and methods to actualize, for example, Rule-based, Content-based, Knowledge Engineering, Data Mining and Collaborative Filtering approach.(2) Collaborative Filtering recommendation arithmetic is researched, including User-based and Item-based recommendation arithmetic. Then it is appointed that collaborative filtering approach suffer from many challenges, such as: sparsity, scalability and cold-start problem.(3) The improved method of Collaborative Filtering recommendation is posed. It unites the ideas of User-based and Item-based recommendation arithmetic. It evaluates ratings of items by the similar items and may solve the problems such as sparsity. Further more, it calculates the nearest neighbors of target user by an improved way that only considers the records with high ratings. As result, it may get accurate results of Personality Recommendation quickly.