融合社交网络的协同过滤推荐算法的研究与应用

发布时间：2018-04-17 19:51

本文选题：社交网络 + 大数据　；参考：《重庆邮电大学》2016年硕士论文

【摘要】：随着社交网络的飞速发展,互联网用户所面临的信息过载问题尤为严重,因此国内外各大社交网络、电子商务等平台都相继推出了个性化推荐系统,以缓解海量数据带来的“选择困难症”问题。随着推荐系统用户关系愈加复杂化,传统的推荐算法已经不能满足当前多数平台的推荐需求。因此,研究大数据时代背景下的推荐策略,在缓解数据压力的同时提高推荐系统的综合质量具有重要的意义。论文的主要研究内容和应用价值如下:通过调研推荐技术现状,在研究各并行平台实现机理基础之上,分析了当前大数据推荐算法,提出了面向大数据的推荐系统双引擎。此外,根据具体算法的特点,例如是否迭代、算法复杂度等,通过调节数据规模等因素进行对比实验,分析不同框架下特定算法的性能,设计了特定场景下最适配的面向大数据的双擎推荐系统框架。双引擎则作为组件按需装配到该框架中,根据需求动态提供单机或者分布式推荐服务,通过对比和调用分析案例验证了该框架的有效性和实用性。基于上述面向大数据的双擎推荐系统框架,深入研究协同过滤推荐算法存在的不足之处。针对用户相似度计算方法单一的问题,分析了社交网络数据的可用性,引入社交网络用户关系作为计算依据,通过构建新的相似度计算规则,融合社交网络与推荐系统的关键要素,提出了融合社交网络多属性的协同过滤推荐算法,为实施推荐的目标用户搜索到了较为准确的邻居集合,进而提高了推荐的准确度。实验结果表明,该推荐算法有效地利用了社交网络元素,在相似度计算方法上较传统方法更为准确和个性化。在算法应用研究中实现了个性化推荐,丰富了推荐系统的结果解释,提高了推荐系统的综合质量。综上所述,本文对融合社交网络的协同过滤推荐算法的研究与应用展开了积极的探索和深入的研究,设计了一种面向大数据的双擎推荐系统框架,提出了融合社交网络多属性的协同过滤推荐算法,并且设计和开发了一个社交网络推荐系统,丰富了基于社交网络的协同过滤推荐算法的理论研究。
[Abstract]:With the rapid development of social network, the problem of information overload faced by Internet users is particularly serious. Therefore, various platforms such as domestic and foreign social networks, e-commerce and other platforms have launched personalized recommendation system.To alleviate the mass of data caused by the "difficult to choose" problem.With the complexity of user relationship in recommendation systems, the traditional recommendation algorithms can not meet the current recommendation needs of most platforms.Therefore, it is of great significance to study the recommendation strategy under the background of big data, which can relieve the pressure of data and improve the comprehensive quality of recommendation system.The main contents and application value of this paper are as follows: based on the research of the status quo of recommendation technology and on the basis of studying the realization mechanism of each parallel platform, this paper analyzes the current big data recommendation algorithm, and puts forward the double engine of the recommended system for big data.In addition, according to the characteristics of the specific algorithm, such as iterative or not, the complexity of the algorithm, by adjusting the data scale and other factors to carry out comparative experiments, the performance of the specific algorithm under different frameworks is analyzed.This paper designs the most suitable double-platform recommendation system framework for big data in a specific scenario.The dual engine is assembled into the framework as a component on demand and provides a single machine or distributed recommendation service dynamically according to the requirements. The effectiveness and practicability of the framework are verified by comparison and call analysis cases.Based on the framework of big data-oriented recommendation system, the shortcomings of collaborative filtering recommendation algorithm are studied.In order to solve the problem of simple similarity calculation method, this paper analyzes the usability of social network data, introduces the user relationship of social network as the basis of calculation, and constructs a new similarity calculation rule.Combining the key elements of social network and recommendation system, a collaborative filtering recommendation algorithm based on multiple attributes of social network is proposed, which can search the accurate neighbor set for the target user of recommendation, and then improve the accuracy of recommendation.Experimental results show that the proposed recommendation algorithm is more accurate and individualized than the traditional method in computing similarity by using the elements of social network effectively.The personalized recommendation is realized in the research of algorithm application, which enriches the interpretation of the recommendation system and improves the comprehensive quality of the recommendation system.To sum up, this paper has carried out active exploration and deep research on the research and application of collaborative filtering recommendation algorithm based on social network, and designed a framework of dual-level recommendation system for big data.A collaborative filtering recommendation algorithm based on multiple attributes of social network is proposed, and a recommendation system of social network is designed and developed, which enriches the theoretical research of collaborative filtering recommendation algorithm based on social network.
【学位授予单位】：重庆邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.3

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