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基于Hypergraph的局部图切割的E尾产品收益预测

E‑tail Product Return Prediction via Hypergraph‑based Local Graph Cut
课程网址: http://videolectures.net/kdd2018_zhu_e-tail_product/  
主讲教师: Yada Zhu
开课单位: 沃森研究中心
开课时间: 2018-11-23
课程语种: 英语
中文简介:
近几十年来,电子商务迅猛发展。特别是,E-tail为客户提供了极大的便利,允许他们在任何地方购买零售产品,而无需访问实际商店。E-tail最近的一个趋势是允许免费送货和无忧退货,以进一步吸引在线客户。然而,这种客户友好政策的一个缺点是退货率迅速增加,以及处理退回的在线订单的相关成本。因此,必须采取积极措施降低回报率和相关成本。尽管从历史购买和退货记录中可以获得大量数据,但到目前为止,E-tail产品退货预测问题尚未引起数据挖掘界的太多关注。为了解决这个问题,在本文中,我们提出了一个名为HyperGo的E尾产品收益预测通用框架。其目的是预测顾客在组装购物篮后返回的意图。对于具有高回报意愿的篮子,电子零售商可以采取适当措施,激励客户不要退货和/或准备逆向物流。所提出的HyperGo基于历史购买和退货记录的新颖超图表示,有效地利用了篮子组成的丰富信息。对于给定的篮子,我们提出了一种局部图切割算法,该算法使用超图上的截断随机游动来识别相似的历史篮子。基于这些篮子,HyperGo能够在两个层面上估计退货意向:篮子层面与产品层面,这为电子裁缝提供了有关潜在退货原因的详细信息(例如,不同颜色的重复产品)。所提出的局部算法的一个主要优点在于其时间复杂度,其线性依赖于输出簇的大小,而多对数依赖于超图的体积。这使得HyperGo特别适合处理大规模数据集。在多个真实世界E尾数据集上的实验结果证明了HyperGo的有效性和效率。
课程简介: Recent decades have witnessed the rapid growth of E-commerce. In particular, E-tail has provided customers with great convenience by allowing them to purchase retail products anywhere without visiting the actual stores. A recent trend in E-tail is to allow free shipping and hassle-free returns to further attract online customers. However, a downside of such a customer-friendly policy is the rapidly increasing return rate as well as the associated costs of handling returned online orders. Therefore, it has become imperative to take proactive measures for reducing the return rate and the associated cost. Despite the large amount of data available from historical purchase and return records, up until now, the problem of E-tail product return prediction has not attracted much attention from the data mining community. To address this problem, in this paper, we propose a generic framework for E-tail product return prediction named HyperGo. It aims to predict the customer’s intention to return after s/he has put together the shopping basket. For the baskets with a high return intention, the E-tailers can then take appropriate measures to incentivize the customer not to issue a return and/or prepare for reverse logistics. The proposed HyperGo is based on a novel hypergraph representation of historical purchase and return records, effectively leveraging the rich information of basket composition. For a given basket, we propose a local graph cut algorithm using truncated random walk on the hypergraph to identify similar historical baskets. Based on these baskets, HyperGo is able to estimate the return intention on two levels: basket-level vs. product-level, which provides the E-tailers with detailed information regarding the reason for a potential return (e.g., duplicate products with different colors). One major benefit of the proposed local algorithm lies in its time complexity, which is linearly dependent on the size of the output cluster and polylogarithmically dependent on the volume of the hypergraph. This makes HyperGo particularly suitable for processing large-scale data sets. The experimental results on multiple real-world E-tail data sets demonstrate the effectiveness and efficiency of HyperGo.
关 键 词: 基于Hypergraph; 局部图切割; E尾产品收益预测; 处理大规模数据集
课程来源: 视频讲座网
数据采集: 2023-03-15:cyh
最后编审: 2023-03-15:cyh
阅读次数: 26