A novel consumer preference mining method based on improved weclat algorithm

IF 2.4 Q3 BUSINESS
J. Qi, Xinwei Mou, Yue Li, Xiaoquan Chu, Weisong Mu
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引用次数: 1

Abstract

Purpose Conventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the relevant applied studies, which leads to failure to obtain some association rules with lower support but from higher-value consumers. Because not all customers are financially attractive to firms, it is necessary that their values be determined and that transactions be weighted. The purpose of this study is to propose a novel consumer preference mining method based on conventional frequent itemsets mining, which can discover more rules from the high-value consumers. Design/methodology/approach In this study, the authors extend the conventional association rule problem by associating the “annual purchase amount” – “price preference” (AP) weight with a consumer to reflect the consumer’s contribution to a market. Furthermore, a novel consumer preference mining method, the AP-weclat algorithm, is proposed by introducing the AP weight into the weclat algorithm for discovering frequent itemsets with higher values. Findings The experimental results from the survey data revealed that compared with the weclat algorithm, the AP-weclat algorithm can make some association rules with low support but a large contribution to a market pass the screening by assigning different weights to consumers in the process of frequent itemsets generation. In addition, some valuable preference combinations can be provided for related practitioners to refer to. Originality/value This study is the first to introduce the AP-weclat algorithm for discovering frequent itemsets from transactions through considering AP weight. Moreover, the AP-weclat algorithm can be considered for application in other markets.
一种基于改进weclat算法的消费者偏好挖掘方法
目的在大多数相关的应用研究中,传统的频繁项目集挖掘忽略了属于不同客户的“交易”的相对收益或重要性是不同的,这导致无法从价值较高的消费者那里获得一些支持度较低的关联规则。由于并非所有客户对公司都具有财务吸引力,因此有必要确定其价值并对交易进行加权。本研究的目的是在传统频繁项目集挖掘的基础上,提出一种新的消费者偏好挖掘方法,可以从高价值消费者身上发现更多的规则。设计/方法论/方法在本研究中,作者通过将“年度购买金额”-“价格偏好”(AP)权重与消费者联系起来,来反映消费者对市场的贡献,从而扩展了传统的关联规则问题。此外,通过在weclat算法中引入AP权重,提出了一种新的消费者偏好挖掘方法,即AP-weclat算法,用于发现具有更高值的频繁项目集。调查数据的实验结果表明,与weclat算法相比,AP weclat算法在频繁项目集生成过程中,通过给消费者分配不同的权重,可以使一些支持度较低但对市场贡献较大的关联规则通过筛选。此外,还可以提供一些有价值的偏好组合,供相关从业者参考。Originality/value本研究首次引入了通过考虑AP权重从交易中发现频繁项目集的AP weclat算法。此外,AP-weclat算法可以考虑在其他市场中应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.30
自引率
8.30%
发文量
35
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