基于用户角色的农资供求信息智能推荐系统的研究及实现
发布时间：2018-08-25 09:43
【摘要】：随着互联网技术的蓬勃发展,网上信息资源的数据量也呈现出爆发式增长。在农资交易平台中,农户如何从大量农资商品中找到符合自己需求的商品,以及供应商如何让自己的商品脱颖而出,都是一件非常困难的事情。目前,针对不同角色用户,不同地域用户及用户在不同季节的需求特性,提供满足其个性化要求的信息服务,已成为农资电子商务站点亟需解决的难题。协同过滤推荐系统作为一种重要的个性化服务模式,在互联网领域的应用越来越广泛。本文以农药为例,研究出一种基于用户角色的推荐算法,与传统的推荐算法相比,此种算法综合了农资季节性,地域性,使用特性等特点,更适用于农资推荐。同时,本文将智能推荐技术与农资交易平台相结合,设计实现基于用户角色的农资供求信息智能推荐系统。本文的主要研究内容如下:现有推荐系统及推荐算法发展现状研究。对基于内容的推荐算法、协同过滤推荐算法等相关基础理论进行了较为深入的研究,结合农资电商推荐系统现状,对如何构建适用农资交易平台的个性化推荐算法进行了深入的研究分析。改进的协同过滤算法研究。通过计算I-I相似矩阵及收集用户隐式行为,建立修正的I-U评分矩阵。并构造用户相似度矩阵U-U及Pearson相关系数计算用户相似性,确定最近邻用户,最后生成预测评分及推荐项。农资供求信息智能推荐系统的实现。在建立了实体间的E-R图基础上,对数据表进行详细设计。搭建storm分布式实时计算框架,设计开发集农资购买、个性推荐、订单管理、和基础查询为一体的农资供求信息智能推荐系统。在安徽省“十二五”科技攻关项目课题“面向全程电子商务的农资物流信息化关键技术研发与应用”的支持下,本论文研究成果成功地将基于用户角色的协同过滤推荐算法运用到农资产品个性化推荐的服务中,有效地减少用户的搜索时间,促进了交易的完成。
[Abstract]:With the rapid development of Internet technology, the amount of data of information resources on the Internet also presents explosive growth. In the agricultural material trading platform, it is very difficult for farmers to find the goods that meet their needs from a large number of agricultural products and how suppliers make their commodities stand out. At present, according to the demand characteristics of different users, different regions and users in different seasons, providing information services to meet their personalized requirements has become a difficult problem that needs to be solved in e-commerce sites of agricultural materials. Collaborative filtering recommendation system, as an important personalized service model, is more and more widely used in the field of Internet. In this paper, a recommendation algorithm based on user's role is developed by taking pesticide as an example. Compared with the traditional recommendation algorithm, this algorithm combines the characteristics of seasonality, regionality and use characteristics of agricultural materials, and is more suitable for recommendation of agricultural materials. At the same time, this paper combines the intelligent recommendation technology with the agricultural material trading platform to design and implement the intelligent recommendation system based on the user role of agricultural material supply and demand information. The main contents of this paper are as follows: the current status of recommendation systems and recommendation algorithms. Based on the content of the recommendation algorithm, collaborative filtering recommendation algorithm and other related basic theory is more in-depth research, combined with the status quo of agricultural e-commerce recommendation system, This paper analyzes how to construct personalized recommendation algorithm which is suitable for agricultural material trading platform. Research on improved collaborative filtering algorithm. By calculating I-I similarity matrix and collecting implicit behavior of users, a modified I-U score matrix is established. The user similarity matrix U-U and Pearson correlation coefficient are constructed to calculate the user similarity, and the nearest neighbor user is determined. Finally, the prediction score and recommendation items are generated. The realization of the intelligent recommendation system of agricultural supply and demand information. Based on the establishment of E-R graph between entities, the data table is designed in detail. A storm distributed real-time computing framework is built to design and develop an intelligent recommendation system for agricultural material supply and demand information, which integrates agricultural material purchase, individual recommendation, order management and basic query. Supported by the "12th Five-Year Plan" key Science and Technology Project in Anhui Province, "Research, development and application of key technologies for agricultural material logistics information oriented to the whole process of electronic commerce", In this paper, the collaborative filtering recommendation algorithm based on user role is successfully applied to the personalized recommendation service of agricultural products, which can effectively reduce the search time of users and promote the completion of transactions.
【学位授予单位】：安徽农业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.3
[Abstract]:With the rapid development of Internet technology, the amount of data of information resources on the Internet also presents explosive growth. In the agricultural material trading platform, it is very difficult for farmers to find the goods that meet their needs from a large number of agricultural products and how suppliers make their commodities stand out. At present, according to the demand characteristics of different users, different regions and users in different seasons, providing information services to meet their personalized requirements has become a difficult problem that needs to be solved in e-commerce sites of agricultural materials. Collaborative filtering recommendation system, as an important personalized service model, is more and more widely used in the field of Internet. In this paper, a recommendation algorithm based on user's role is developed by taking pesticide as an example. Compared with the traditional recommendation algorithm, this algorithm combines the characteristics of seasonality, regionality and use characteristics of agricultural materials, and is more suitable for recommendation of agricultural materials. At the same time, this paper combines the intelligent recommendation technology with the agricultural material trading platform to design and implement the intelligent recommendation system based on the user role of agricultural material supply and demand information. The main contents of this paper are as follows: the current status of recommendation systems and recommendation algorithms. Based on the content of the recommendation algorithm, collaborative filtering recommendation algorithm and other related basic theory is more in-depth research, combined with the status quo of agricultural e-commerce recommendation system, This paper analyzes how to construct personalized recommendation algorithm which is suitable for agricultural material trading platform. Research on improved collaborative filtering algorithm. By calculating I-I similarity matrix and collecting implicit behavior of users, a modified I-U score matrix is established. The user similarity matrix U-U and Pearson correlation coefficient are constructed to calculate the user similarity, and the nearest neighbor user is determined. Finally, the prediction score and recommendation items are generated. The realization of the intelligent recommendation system of agricultural supply and demand information. Based on the establishment of E-R graph between entities, the data table is designed in detail. A storm distributed real-time computing framework is built to design and develop an intelligent recommendation system for agricultural material supply and demand information, which integrates agricultural material purchase, individual recommendation, order management and basic query. Supported by the "12th Five-Year Plan" key Science and Technology Project in Anhui Province, "Research, development and application of key technologies for agricultural material logistics information oriented to the whole process of electronic commerce", In this paper, the collaborative filtering recommendation algorithm based on user role is successfully applied to the personalized recommendation service of agricultural products, which can effectively reduce the search time of users and promote the completion of transactions.
【学位授予单位】：安徽农业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.3
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