The Design And Implementation Of Recommender System Of Mobile Phone Based On Android Platform

The emergence and popularity of mobile Internet is thrown us into an era of data explosion. Massive data resources are meet the user’s need, at the same timebrings the problem of data resources overload. With the substantial growth of the data on the network, people want to find what they are need as difficult as looking for a needle in a bottle of hay. Thus,the efficiency of the using information has been reduced.At present, one of the solutions for the problem of data resource surplusis the information push system, which is a represented of the recommendation engine. The information push system can matchthe products which users may want to buy and recommend it to the user. Compared with the search system, the recommendation system is generally running in the background to monitoring and recording user’s behavior data, it analysis those data and sum up the rules, so as to solve the problem of search engine which cannot be deep understanding of user personalization information, potential users information, vertical one-way transmission of information.At the present stage, most of the commercial recommendation engine architecture is based on the server.In principle, this type of recommendation engine is a web server for user behavior data collection and modeling. It is not only takes up resources of the web server system which adding the cost of overhead and limitedthe behavior data collection, but also may lead to leakage of user information which brings potential hazards to the user’s privacy.This paper is based on the above background to developing a personalized recommendation engine that was applied on the android platform.The AC algorithm and Trie tree data structure are used to optimize this recommendation engine.Mining the key words to describe user’s preferences and needs by analyze the short message content on the user’s mobile phone and the contents of the English text downloaded to the phone. It is match the mining information and the ad content in the memory to recommend the information to the user which they are interested in or need. In addition, the combination of the personalized recommendation engine and android system are also provides a great space for future business promotion.