A Mixture Of Personalized Recommendation Research And Application In The Mobile Search

As the Internet business applications using more and more, the web-based search engine has become an important tool for people get information, because of it has highly efficient, convenient and direct access. However, the Internet information is distributed, heterogeneous, open, and rapid accumulation, and the information is varying colossal,quality and distribution of scattered. It will cause user can not focus on the useful information. In order to improve the user experience, the personalized recommendation of search engineer which based on user characteristics and behavior are become more and more common.Recently, more and more feature-rich mobile device come to the market, users can use the mobile search engineer to get useful information but not depend on any location and networks. In another side, the mobile device has smaller screen and weak processing capacity. This will result in mobile search need more accurate and concentrated, then it can display the useful information in such a small screen. Therefore, the personalized recommendation technology in the field of mobile search application is even more important.This paper is in the context of this, to learn and research the personalized recommendations related technologies and theories, analyzes personalized recommendation technology in the mobile search industry. After studying a number of recommendation methods and according to the features of mlnfo mobile search system, the paper designed a personalized recommended method,which mix of Utility-based Recommendation,Collaborative Filtering Recommendation and Association Rule based Recommendation. And applied the method to the WAP Search of mInfo mobile search system to Reduce the difficulty of using,improve customer satisfaction and mine the user's potential demands.