Design And Implementation Of Intelligent Recommendation System

With the development of mobile Internet technology and maturity of mobile terminal in recentyears, it appears to be a rapid growth of the number of intelligent mobile phone users. More andmore people tend to use smart mobile phone to get information, to communicate, to shareinformation, or to get help. There is no doubt that mobile Internet has made our life moreconvenient and exciting. However, simultaneously, it produces a large amount of influx information.Hence people are forced to have stepped into an “information overload” world. When surroundedby too much information, it is hard to get access to the information that the users are interested inefficiently, which virtually causes a great deal of trouble to users. Consequently, the personalizedrecommendation system was proposed.In this thesis, the personalized recommendation system is designed and implemented. It isbased on Android system and meets the various of needs in order. Meanwhile, the personalizedrecommendation service can automatically push dishes to users based on their preferences.The main research works in this thesis are as follows:Firstly, traditional collaborative filtering algorithm has problems such as cold-start and datasparsity. To solve these problems, an improved collaborative filtering algorithm that based on usersegmentation and time weighting is proposed.Secondly, a simulation is conducted according to the algorithm above. The result shows thatthe algorithm can help to solve problems of cold-start and data sparsity.Thirdly, the thesis designs and implements the system and the test result proves the system canwork well.