以产品为中心的客户定向机制
发布时间：2019-03-05 13:51
【摘要】：随着互联网的不断发展,以产品为中心寻找潜在客户已经成为众多领域的重要应用之一,特别是Web 2.0时代的到来。本文旨在研究以商户为中心的潜在客户定向问题,提出了一个产品-客户的匹配框架。一般来说,要完成用户和产品之间的匹配主要包括两个模块,第一个模块是构造用户的兴趣偏好,第二个模块是执行以产品为驱动的查询,也称逆向排名查询。因此,本文的框架主要包括了两个阶段：数据预处理和查询处理。数据预处理阶段设计了简单的规则算法来学习用户的兴趣偏好。查询处理阶段,使用逆向k排名查询和逆向Top-k-Ranks查询两种查询来为给定的产品找到匹配的用户。本文的主要贡献如下：·基于规则方法的用户兴趣偏好学习.随着Web2.0应用的发展,越来越多的用户参与其中对消费过的产品进行打分评论。本文的研究场景主要是针对大众点评、美团、Yelp和世纪佳缘等生活类平台,由于其中的评论数据由于文本短、数据稀疏,要利用评论数据构造偏好无疑具有很大的挑战。幸运的是,用户对产品各个属性的打分可以直观地提炼出用户对产品的喜好程度。因此,本文主要利用打分信息来构造用户的兴趣偏好,并在原有线性模型表示用户喜好程度的基础之上提出了扩展的线性模型。·逆向k排名查询.本文提出了以产品为驱动的逆向k排名查询。这个查询是面向所有产品集合,使用的是线性模型,以每个用户在给定产品下的排名作为排序的基准,为每一个产品找到排序靠前的k个用户。本文在多维场景下为这个查询提出了三种算法,包括基于树的剪枝方法(Tree-based Pruning Approach, TPA),批量剪枝算法(Batch Pruning Approach, BPA)和标记的剪枝算法(Marked Pruning Approach, MPA).考虑到二维场景下平面几何的特殊性质,本文设计了两种算法,包括基于排序的算法(Sorting-based approach, SA)和基于树的算法(Tree-based Approach, TBA).最后,论文分别使用人工数据集和真实数据集做了大量的实验来对算法的有效性进行了验证。·逆向Top-k-Ranks查询.本文提出了面向产品的逆向Top-k-Ranks查询。这个查询整合了逆向top-k查询和逆向k排名查询,使用的是扩展的线性模型,通过计算用户和产品的得分值得到每个用户相对于查询产品的排名,依据排名值,返回最喜欢这个查询产品的k个用户以及把这个产品列入top k集合的所有用户。本文为这个查询设计了两种算法,包括扩展的RTA方法(Extended RTA, ERTA)和基于历史信息的批量剪枝算法(History-Based Batch Pruning Approach, HBPA).最后,论文分别使用人工数据集和真实数据集做了大量的实验来对算法的有效性进行了验证。综上所述,本文针对面向产品的客户定向机制重点研究了如何构建用户兴趣偏好向量、面向线性模型的逆向k排名查询和面向扩展模型的逆向Top-k-Ranks查询三个问题,提出了解决这个问题的通用匹配框架。本文对提出的模型及算法在理论分析的基础之上,分别在真实数据集和人工数据集上进行了验证,结果表明本文提出的解决方法有好的效果。
[Abstract]:With the development of the Internet, it has become one of the most important applications in many fields, especially the arrival of the Web 2.0 era. The purpose of this paper is to study the potential customer orientation problem with the merchant as the center, and put forward a product-customer matching framework. In general, the matching between the user and the product mainly includes two modules, the first module is the user's interest preference, and the second module is to execute the query driven by the product, also called the reverse ranking query. Therefore, the framework of this paper mainly includes two stages: data pre-processing and query processing. A simple rule algorithm is designed in the data pre-processing stage to study the user's interest preference. The query processing stage uses the reverse-k ranking query and the reverse Top-k-Ranks to query the two queries to find a matching user for a given product. The main contributions of this paper are as follows: 路 User interest preference learning based on the rule method. With the development of the Web 2.0 application, more and more users are involved in scoring the consumer products. The research scene of this paper is mainly focused on the public comment, the American League, the Yelp and the Jiayuan Jiayuan, because of the short text and the sparse data, it is undoubtedly a great challenge to make use of the preference of the comment data. Fortunately, the user's scoring of the individual attributes of the product can intuitively refine the user's degree of preference for the product. Therefore, this paper mainly uses the scoring information to construct the user's interest preference, and puts forward the extended linear model on the basis of the original linear model representing the user's preference degree. 路 Reverse-k ranking query. In this paper, a reverse-k ranking query based on product is presented. This query is for all product collections, using a linear model for each user to find the top-ranked k users for each product based on the rank of the given product as a sort reference. In this paper, three algorithms are proposed for this query in a multi-dimensional scene, including tree-based Pruning (TPA), batch pruning (BPA) and marked pruning (MPA). In view of the special properties of the plane geometry in the two-dimensional scene, two algorithms are designed, including the sort-based algorithm (SA) and the tree-based algorithm (TBA). Finally, the paper makes a lot of experiments to verify the validity of the algorithm by using the artificial data set and the real data set. 路閫嗗悜Top-k-Ranks鏌ヨ. In this paper, a reverse Top-k-Ranks query for products is presented. the query integrates a reverse top-k query and a reverse k ranking query, using an extended linear model, Returns the k users who most like this query product and all users who have this product in the top k set. In this paper, two algorithms, including extended RTA (RTA) and historical-based batch pruning (HBPA), are designed for this query. Finally, the paper makes a lot of experiments to verify the validity of the algorithm by using the artificial data set and the real data set. To sum up, this paper focuses on how to build a user interest preference vector, a reverse-k ranking query for a linear model and a reverse Top-k-Ranks query for an extended model, and proposes a universal matching framework to solve this problem. On the basis of the theoretical analysis of the proposed model and algorithm, this paper makes a verification on the real data set and the artificial data set, and the results show that the solution proposed in this paper has good effect.
【学位授予单位】：华东师范大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP311.13;TP393.09
本文编号：2434971
[Abstract]:With the development of the Internet, it has become one of the most important applications in many fields, especially the arrival of the Web 2.0 era. The purpose of this paper is to study the potential customer orientation problem with the merchant as the center, and put forward a product-customer matching framework. In general, the matching between the user and the product mainly includes two modules, the first module is the user's interest preference, and the second module is to execute the query driven by the product, also called the reverse ranking query. Therefore, the framework of this paper mainly includes two stages: data pre-processing and query processing. A simple rule algorithm is designed in the data pre-processing stage to study the user's interest preference. The query processing stage uses the reverse-k ranking query and the reverse Top-k-Ranks to query the two queries to find a matching user for a given product. The main contributions of this paper are as follows: 路 User interest preference learning based on the rule method. With the development of the Web 2.0 application, more and more users are involved in scoring the consumer products. The research scene of this paper is mainly focused on the public comment, the American League, the Yelp and the Jiayuan Jiayuan, because of the short text and the sparse data, it is undoubtedly a great challenge to make use of the preference of the comment data. Fortunately, the user's scoring of the individual attributes of the product can intuitively refine the user's degree of preference for the product. Therefore, this paper mainly uses the scoring information to construct the user's interest preference, and puts forward the extended linear model on the basis of the original linear model representing the user's preference degree. 路 Reverse-k ranking query. In this paper, a reverse-k ranking query based on product is presented. This query is for all product collections, using a linear model for each user to find the top-ranked k users for each product based on the rank of the given product as a sort reference. In this paper, three algorithms are proposed for this query in a multi-dimensional scene, including tree-based Pruning (TPA), batch pruning (BPA) and marked pruning (MPA). In view of the special properties of the plane geometry in the two-dimensional scene, two algorithms are designed, including the sort-based algorithm (SA) and the tree-based algorithm (TBA). Finally, the paper makes a lot of experiments to verify the validity of the algorithm by using the artificial data set and the real data set. 路閫嗗悜Top-k-Ranks鏌ヨ. In this paper, a reverse Top-k-Ranks query for products is presented. the query integrates a reverse top-k query and a reverse k ranking query, using an extended linear model, Returns the k users who most like this query product and all users who have this product in the top k set. In this paper, two algorithms, including extended RTA (RTA) and historical-based batch pruning (HBPA), are designed for this query. Finally, the paper makes a lot of experiments to verify the validity of the algorithm by using the artificial data set and the real data set. To sum up, this paper focuses on how to build a user interest preference vector, a reverse-k ranking query for a linear model and a reverse Top-k-Ranks query for an extended model, and proposes a universal matching framework to solve this problem. On the basis of the theoretical analysis of the proposed model and algorithm, this paper makes a verification on the real data set and the artificial data set, and the results show that the solution proposed in this paper has good effect.
【学位授予单位】：华东师范大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP311.13;TP393.09
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