Research And Application Of Mobile Application Based On Social Networking Service

With the rapid growth and wide popularization of the Internet, mobilecommunication technology and the smart mobile terminals like mobile phones andtablet PCs, the number of mobile internet users has enjoyed explosive growth. Going farbeyond the traditional PC-based internet access, the mobile internet has become themajor internet terminal.As a special e-commerce platform, the mobile application market has beenbooming in recent years. From the mobile application market, people can install variousapplications, which provides great convenience and pleasure to their work, study andlife. The number of applications in some major markets is reaching or has reached onemillion, and searching some applications users are interested in among the vast list hasturned out to be a trouble to most normal users. Traditional searching and classificationfall to heighten the enthusiasm of users to buy or install the application, whichstimulates application markets to make a change. Although some application marketsoffer recommendations based on the content, and even utilize personalizedrecommendation techniques like collaborative filtering, problems such as cold start anddata sparsity remain serious. Meanwhile, social networking services, as an internetservice platform facing social network, has been rapidly developing on the internet.Users can establish friendship through “friends of friends”, and they also can makefriends with those who are unfamiliar but share the similar characteristics and interests.Social network community has gained great popularity, and it will place an increasinglyimportant role online or offline.For the data sparsity in the process of personalized recommendation on mobileapplication market, especially in the collaborative filtering recommendation, afterstudying the related theory and technology of social network and collaborative filteringrecommendation, this study presents and verifies a model which is based on socialnetwork’s collaborative filtering recommendation. On this basis, it also sets out thedesign and implementation of a system prototype for mobile applicationrecommendation based on the social network.This paper has accomplished the following tasks:①The paper studies the research status at home and abroad about personalizedrecommendation. For the application scenarios in this study, the paper researches related theories about Android development, techniques about personalized recommendationand social network.②After comparing advantages, defects and usage scenarios of three kinds ofcollaborative filtering recommendations modes, this paper analyzes the acquisition andcompute mode of the user interest model in the social network, and sets out acollaborative filtering recommendation model based on social network and then verifyit.③For the model of recommendation, this paper designs mobile side and serverside of mobile application recommendation system based on social network. The mobileside is used to collect user data, corresponding requests and etc., while the server side isused for data analysis and processing of recommendation model.④The major module prototypes of mobile application recommendation systemare designed and implemented in this paper, including data acquisition of usersoperation, the display of recommended applications from the client side, as well asmodules of both social network and recommendation from the server side.⑤The prototype system and the feasibility and effectiveness of the model designare also verified through examples. After analyzing the prototype, the paper summarizesthe points and reaches the conclusion.