基于用户消费习惯的推荐算法研究
发布时间：2019-06-03 22:47
【摘要】：随着Web2.0的飞速发展,网络中的信息也在以前所未有的规模增长。尤其在电子商务领域,海量商品出现在各种电子商务网站上。普通用户需要的是从大量的信息流中实时获取到对自身有用的信息,让生活更加简单;商家需要的是将自家的产品有效地推荐给适合的群体,使得商家获得最大的利益。在这样的背景下,推荐算法的研究,尤其是基于用户消费习惯的推荐算法研究就显得十分重要。本文的主要工作有以下两个方面:1)基于用户消费价格的评分预测算法研究。评分预测问题是推荐系统研究中的一个热点问题,评分预测算法使用各种辅助信息来提升推荐效果。电子商务网站存在大量的用户消费价格信息,本文利用用户的消费价格信息来提升评分预测的效果。通过对于这类数据的分析,本文发现了用户评分与消费价格信息之间的三条关联,同时本文将这三条关联与矩阵分解算法相结合,提出了基于价格敏感的评分推荐算法。除此之外,本文通过增广矩阵和价格离散化的方法很好地解决了数据稀疏性问题以及价格信息的噪音问题。基于大众点评的数据集,本文针对提出的基于价格敏感的评分推荐算法做了大量实验。通过与众多经典算法相比较,本文验证了基于价格敏感的评分推荐算法的有效性。同时,通过研究不同参数的物理意义,深入地研究了评分与价格间的关系。2)基于用户消费行为的潜在重复购买用户推荐算法研究。很多商家都会通过在电子商务网站上打折的方式吸引新用户。商家希望吸引到的新用户可以转化为重复购买用户,而大多数新用户只是一次性购买用户。本文提出了一个多分类模型融合的推荐算法,利用这个算法为商家推荐潜在的重复购买用户。在这个算法中,本文设计了一种二层级联的多分类模型融合框架。在第一层中,本文通过使用多种不同类型的分类器以及特征组合,产生了大量不同的分类结果。在第二层中,本文使用逻辑斯特回归的分类方法将第一层的多种分类结果相互融合,达到更好的推荐效果。通过在天猫商城真实数据集上的实验,本文验证了算法的有效性。同时,为了将算法部署到云计算平台,本文在特征提取阶段,对算法做了相应的并行化设计。
[Abstract]:With the rapid development of Web2.0, the information in the network is also growing on an unprecedented scale. Especially in the field of e-commerce, a large number of goods appear on a variety of e-commerce websites. What ordinary users need is to obtain useful information for themselves in real time from a large number of information flows, so that life is simpler; what businesses need is to effectively recommend their own products to the appropriate groups, so that businesses can get the greatest benefits. In this context, the research of recommendation algorithm, especially the recommendation algorithm based on user consumption habit, is very important. The main work of this paper is as follows: 1) Research on scoring prediction algorithm based on user consumer price. Scoring prediction problem is a hot issue in the research of recommendation system. Scoring prediction algorithm uses a variety of auxiliary information to improve the recommendation effect. There are a lot of consumer price information in e-commerce website. This paper uses the consumer price information of users to improve the effect of rating prediction. Through the analysis of this kind of data, this paper finds three associations between user score and consumer price information. At the same time, this paper combines these three associations with matrix decomposition algorithm, and proposes a rating recommendation algorithm based on price sensitivity. In addition, this paper solves the problem of data sparsity and the noise of price information by means of augmented matrix and price discretization. Based on Dianping's dataset, a large number of experiments are carried out on the proposed price-sensitive rating recommendation algorithm. Compared with many classical algorithms, this paper verifies the effectiveness of the price-sensitive rating recommendation algorithm. At the same time, by studying the physical meaning of different parameters, the relationship between score and price is deeply studied. 2) Research on potential repeat purchase user recommendation algorithm based on user consumption behavior. Many businesses attract new users by offering discounts on e-commerce sites. Businesses want to attract new users who can be converted to repeat buyers, while most new users buy users at once. In this paper, a recommendation algorithm based on multi-classification model fusion is proposed, which is used to recommend potential repeat purchase users for businesses. In this algorithm, a two-layer cascade multi-classification model fusion framework is designed. In the first layer, many different types of classifiers and feature combinations are used to produce a large number of different classification results. In the second layer, this paper uses the logical regression classification method to merge the classification results of the first layer with each other to achieve better recommendation results. The effectiveness of the algorithm is verified by experiments on the real data set of Tmall Mall. At the same time, in order to deploy the algorithm to cloud computing platform, this paper makes the corresponding parallelization design of the algorithm in the feature extraction stage.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.3
本文编号：2492278
[Abstract]:With the rapid development of Web2.0, the information in the network is also growing on an unprecedented scale. Especially in the field of e-commerce, a large number of goods appear on a variety of e-commerce websites. What ordinary users need is to obtain useful information for themselves in real time from a large number of information flows, so that life is simpler; what businesses need is to effectively recommend their own products to the appropriate groups, so that businesses can get the greatest benefits. In this context, the research of recommendation algorithm, especially the recommendation algorithm based on user consumption habit, is very important. The main work of this paper is as follows: 1) Research on scoring prediction algorithm based on user consumer price. Scoring prediction problem is a hot issue in the research of recommendation system. Scoring prediction algorithm uses a variety of auxiliary information to improve the recommendation effect. There are a lot of consumer price information in e-commerce website. This paper uses the consumer price information of users to improve the effect of rating prediction. Through the analysis of this kind of data, this paper finds three associations between user score and consumer price information. At the same time, this paper combines these three associations with matrix decomposition algorithm, and proposes a rating recommendation algorithm based on price sensitivity. In addition, this paper solves the problem of data sparsity and the noise of price information by means of augmented matrix and price discretization. Based on Dianping's dataset, a large number of experiments are carried out on the proposed price-sensitive rating recommendation algorithm. Compared with many classical algorithms, this paper verifies the effectiveness of the price-sensitive rating recommendation algorithm. At the same time, by studying the physical meaning of different parameters, the relationship between score and price is deeply studied. 2) Research on potential repeat purchase user recommendation algorithm based on user consumption behavior. Many businesses attract new users by offering discounts on e-commerce sites. Businesses want to attract new users who can be converted to repeat buyers, while most new users buy users at once. In this paper, a recommendation algorithm based on multi-classification model fusion is proposed, which is used to recommend potential repeat purchase users for businesses. In this algorithm, a two-layer cascade multi-classification model fusion framework is designed. In the first layer, many different types of classifiers and feature combinations are used to produce a large number of different classification results. In the second layer, this paper uses the logical regression classification method to merge the classification results of the first layer with each other to achieve better recommendation results. The effectiveness of the algorithm is verified by experiments on the real data set of Tmall Mall. At the same time, in order to deploy the algorithm to cloud computing platform, this paper makes the corresponding parallelization design of the algorithm in the feature extraction stage.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.3
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