网络购物中顾客共同趋向性获取算法研究

发布时间：2018-02-27 00:32

本文关键词： 网络购物相似离度加权 RA 链路预测共同趋向性　出处：《首都经济贸易大学》2015年硕士论文　论文类型：学位论文

【摘要】：电子商务的迅猛发展,让网络购物成为一种趋势,但身处大数据时代,面对海量的商品信息,很容易让顾客的购物兴趣减弱。推荐系统的出现在一定程度上解决了这个问题,而推荐算法在推荐系统中扮演着重要的角色。本文运用集合相似度理论、复杂网络的相关理论及链路预测的知识,从顾客的角度出发,利用历史购买记录对顾客关系进行分析,运用加权RA消除非常规顾客对相似性计算的影响。通过进行链路预测的方法,构建顾客共同趋向性获取算法,进而基于相关顾客购物共同趋向性得到目标顾客最有可能购买的商品。通过将所建模型的求解结果与实验验证结果进行对比分析,得出本文算法的可行性。对于模型的构建主要运用以下方法:(1)集合运算。通过让大学生作为顾客进行商品购买,从而得到顾客商品集合,集合运算得到顾客商品关系矩阵。(2)相似性算法。针对顾客是否为孤立点这两种情况,综合运用余弦相似系数和相对欧式距离系数进行顾客相似离度的求解,既考虑了样本内数据变化规律的差异也考虑了样本数据的数值差异。为了消除主流顾客对顾客相似性计算的影响,在此基础上将相似离度值作为权重进行加权RA的计算,运用pajek构建顾客相似关系网络。(3)共同趋向性获取算法。针对最相似顾客数量的不同分别进行推荐,基于相关顾客购物共同趋向性得到目标顾客最有可能购买的商品。
[Abstract]:With the rapid development of electronic commerce, online shopping has become a trend, but in the era of big data, it is easy to weaken the customer's interest in shopping in the face of massive commodity information. The appearance of recommendation system solves this problem to a certain extent. The recommendation algorithm plays an important role in the recommendation system. Using the theory of set similarity, the related theory of complex network and the knowledge of link prediction, from the customer's point of view, using the historical purchase record to analyze the customer relationship, this paper analyzes the relationship of customer by using the theory of set similarity, the theory of complex network and the knowledge of link prediction. The weighted RA is used to eliminate the influence of unconventional customers on similarity calculation. Through the method of link prediction, a customer common trend acquisition algorithm is constructed. Then, based on the common tendency of relevant customers, we get the most likely items to be purchased by the target customers. The results of the model are compared with the experimental results. The feasibility of this algorithm is obtained. The following method is mainly used to construct the model: 1) set operation. By making college students buy goods as customers, we can get the set of customers. The similarity algorithm of customer's merchandise relation matrix is obtained by set operation. In view of whether the customer is an outlier or not, the similarity degree of customer is solved by using cosine similarity coefficient and relative Euclidean distance coefficient synthetically. In order to eliminate the influence of mainstream customers on customer similarity calculation, the similarity deviation value is used as the weight to calculate the weighted RA in order to eliminate the influence of mainstream customers on customer similarity calculation. Using pajek to construct customer similarity relationship network. (3) Common trend acquisition algorithm. According to the different number of most similar customers, we recommend the most likely products for the target customers based on the common trend of customer shopping.
【学位授予单位】：首都经济贸易大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP391.3;F724.6

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