基于区域社交网络的信息分级系统的研究与应用
发布时间：2018-09-17 20:11
【摘要】：随着近十几年互联网的高速发展,社交网络已经变成人与人之间沟通交流的桥梁,其对人们的信息获取、思考和生活产生了不可估量的影响。随着移动终端设备的不断升级以及移动平台操作系统的逐步完善,基于LBS的移动社交网络由于其操作方便、信息快捷等优势,在互联网大家族中扮演着越来越重要的角色。而在最近几年中,移动社交网络逐渐朝着小众化、创新化发展,给用户带来定制化的用户体验。在如此背景下,为了满足某个区域内人们之间的信息交流、沟通互动,本文提出了一种基于区域的社交模式:区域内用户之间多对多实时交流。一个用户所发布的信息可以被区域内其他所有用户获取并评论,实现一种相对自由的区域信息分享平台。但这种模式下,用户接受的信息体量会十分庞大,在如此多的内容中,用户会被大量无感的信息所包围,所以本文提出了针对这种社交模式的信息分级系统。该信息分级系统核心就是信息推荐,也可以叫做信息推荐系统,其根据用户偏好来推荐该用户感兴趣的信息。本文首先介绍LBS、推荐系统、LDA等相关知识。然后对系统从功能、非功能、其他三个方面做了需求分析,其次从功能模块、系统交互、数据库表等方面对系统做了总体设计。随后针对系统最重要的信息推荐模块展开讨论:对区域信息推荐模块,在提出的模块设计基础上,通过中文文本预处理、LDA主题提取、文档排序等步骤给出了详细实现。对个性化信息推荐模块,本文研究了基于内容的推荐和基于用户的协同过滤两种算法,基于内容的推荐充分利用区域信息推荐中得到的数据,计算信息内容的相似度,而基于用户的协同过滤则通过用户对信息的评分来计算用户之间的相似度。两种算法在计算相似度时都在原有基础上考虑了时间上下文,得到一种新的相似度计算方法。为了测试该相似度计算方法,本文在CCF竞赛数据集上设计实验,通过准确率和召回率的比较,最终验证了新算法的有效性。最后本文将所研究的信息分级推荐系统应用于实践,实现了一个简单的应用,给出了关键性代码以及主要的实现界面,并测试了分级推荐相关功能。
[Abstract]:With the rapid development of Internet in recent years, social network has become a bridge of communication between people, which has an incalculable impact on people's information acquisition, thinking and life. With the continuous upgrading of mobile terminal devices and the gradual improvement of mobile platform operating system, mobile social network based on LBS is playing an increasingly important role in the Internet family because of its advantages of convenient operation and fast information. In recent years, mobile social networks are gradually moving towards a niche and innovative development, bringing customized user experience to users. In this context, in order to meet the information exchange and communication interaction among people in a certain region, this paper proposes a region-based social model: many-to-many real-time communication among users in the region. The information published by a user can be accessed and commented by all other users in the region, thus realizing a relatively free regional information sharing platform. However, in this mode, the amount of information accepted by users will be very large. In such a large amount of content, users will be surrounded by a large number of senseless information, so this paper proposes an information grading system for this social model. The core of the information classification system is information recommendation, which can also be called information recommendation system, which recommends the information that the user is interested in according to the user's preference. This paper first introduces the LBS, recommendation system and other related knowledge. Then the system from the functional, non-functional, the other three aspects of the requirements analysis, followed by the functional module, system interaction, database table and other aspects of the overall design of the system. Then the most important information recommendation module of the system is discussed. On the basis of the design of the proposed module, a detailed implementation is given through the Chinese text preprocessing, LDA topic extraction, document sorting and so on. For the personalized information recommendation module, this paper studies two algorithms: content-based recommendation and user-based collaborative filtering. The content-based recommendation makes full use of the data obtained from the regional information recommendation, and calculates the similarity of the information content. The user-based collaborative filtering computes the similarity between users by scoring the information. The two algorithms take the time context into account when calculating the similarity, and obtain a new similarity calculation method. In order to test the similarity calculation method, this paper designs experiments on the CCF competition data set, and finally verifies the effectiveness of the new algorithm by comparing the accuracy and recall. Finally, this paper applies the information hierarchical recommendation system to practice, realizes a simple application, gives the key code and the main implementation interface, and tests the related functions of hierarchical recommendation.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.3;TP393.09
本文编号：2246964
[Abstract]:With the rapid development of Internet in recent years, social network has become a bridge of communication between people, which has an incalculable impact on people's information acquisition, thinking and life. With the continuous upgrading of mobile terminal devices and the gradual improvement of mobile platform operating system, mobile social network based on LBS is playing an increasingly important role in the Internet family because of its advantages of convenient operation and fast information. In recent years, mobile social networks are gradually moving towards a niche and innovative development, bringing customized user experience to users. In this context, in order to meet the information exchange and communication interaction among people in a certain region, this paper proposes a region-based social model: many-to-many real-time communication among users in the region. The information published by a user can be accessed and commented by all other users in the region, thus realizing a relatively free regional information sharing platform. However, in this mode, the amount of information accepted by users will be very large. In such a large amount of content, users will be surrounded by a large number of senseless information, so this paper proposes an information grading system for this social model. The core of the information classification system is information recommendation, which can also be called information recommendation system, which recommends the information that the user is interested in according to the user's preference. This paper first introduces the LBS, recommendation system and other related knowledge. Then the system from the functional, non-functional, the other three aspects of the requirements analysis, followed by the functional module, system interaction, database table and other aspects of the overall design of the system. Then the most important information recommendation module of the system is discussed. On the basis of the design of the proposed module, a detailed implementation is given through the Chinese text preprocessing, LDA topic extraction, document sorting and so on. For the personalized information recommendation module, this paper studies two algorithms: content-based recommendation and user-based collaborative filtering. The content-based recommendation makes full use of the data obtained from the regional information recommendation, and calculates the similarity of the information content. The user-based collaborative filtering computes the similarity between users by scoring the information. The two algorithms take the time context into account when calculating the similarity, and obtain a new similarity calculation method. In order to test the similarity calculation method, this paper designs experiments on the CCF competition data set, and finally verifies the effectiveness of the new algorithm by comparing the accuracy and recall. Finally, this paper applies the information hierarchical recommendation system to practice, realizes a simple application, gives the key code and the main implementation interface, and tests the related functions of hierarchical recommendation.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.3;TP393.09
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