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1、 电 子 科 技 大 学 UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA 专业学位硕士学位论文 MASTER THESIS FOR PROFESSIONAL DEGREE 论文题目 面向移动互联网营销的推荐系统 研究与应用 专业学位类别 工程硕士 学 号 201122060530 作 者 姓 名 李渭民 指 导 教 师 周 涛 教 授 2 分类号 密级 UDC注1 学 位 论 文 面向移动互联网营销的推荐系统 研究与应用 面向移动互联网营销的推荐系统 研究与应用 李渭民 李渭民 指导教师 周 涛 教 授 周 涛 教 授 电子科技
2、大学 成 都 电子科技大学 成 都 申请学位级别 硕士硕士 专业学位类别 工程硕士工程硕士 工程领域名称 计算机技术计算机技术 提交论文日期 2014-3-24 论文答辩日期 2014-5-14 学位授予单位和日期 电子科技大学电子科技大学 2014 年年 6 月月 29 日日 答辩委员会主席 评阅人 注 1：注明国际十进分类法 UDC的类号。RECOMMENDED SYSTEM RESEARCH AND APPLICATION FOR MOBILE INTERNET MARKETING A Master Thesis Submitted to University of Electronic
3、 Science and Technology of China Major: Computer Technology Author: Weimin Li Advisor: Tao Zhou School : School of Computer Science For information producers, it is also difficult to make the information produced by themselves be extracted from the mass of information. The search engine allows users
4、 to find the information they need by key word, but when a user cannot express what he or she wanted by keyword or his (her) demand is not clear, the search engine appears stretched. Recommendation system is an effective tool to solve this situation , it is different from search engines that it does
5、 not require users to provide a clear demand, it take the users past behavior to train a model for users interest, and then recommend some information to the user that one may interest . Now, Recommendation System is widely used in a lot of fields. In academia, Recommendation Systems are also very p
6、opular. In this paper, we based on a large scale mobile network user behavior data; try to combines it with Recommendation System for mobile advertising. Compared with the electricity supplier data or television program data, mobile network data is more accurately, it can be precisely targeted to a
7、specific person in the real world. In this paper, we combined the data that mobile phone users access App with some ideas of Recommendation System, tried analysis and experiments as follows: 1) Find some algorithm and parameters which is more suitable for mobile network advertising and recommendatio
8、n; ABSTRACT III 2) looking for a data partitioning Approach which not only make lower computational complexity but ensuring the effect of advertising delivery; When training the model parameters, it is feasible for a small amount of data, but when the amount of data is large, the total amount of dat
9、a will lead to large computation load, but also the whole data may not necessarily enhance the performance of the algorithm. After the data which participating the training model reaches a certain amount, the same algorithm cannot dig deeper data on users interest. 3) find some relationship between
10、recommendation algorithm and the amount of training data , to find out how much data make the algorithm effect “saturation“, so that we can adjusting the amount of data and algorithm parameters in engineering Applications, reduce the computational complexity. Keywords: Mobile network, personalized R
11、ecommendation System, advertising 目录 IV 目 录 第一章 绪 论 . 1 1.1 研究的背景 . 1 1.2 论文的主要工作及意义 . 2 1.3 本论文的结构安排 . 3 第二章 移动互联网广告与推荐系统的相关研究进展 . 5 2.1 移动互联网广告 . 5 2.1.1 引言 . 5 2.1.2 移动互联网广告特殊性及其投放方式 . 5 2.1.3 移动互联网广告商业模式剖析 . 7 2.1.4 移动互联网广告市场的优势与劣势 . 8 2.1.5 移动广告发展状况分析 . 9 2.2 推荐系统 . 12 2.2.1 引言 . 12 2.2.2 什么是推荐系统 . 13 2.2.3 推荐算法的分类及介绍 . 14 2.2.4 推荐系统的评价指标 . 17 2.2.5 推荐系统的应用 .
