【摘要】 【目的】对商品检索中的购物任务进行识别,并对多任务会话行为特征进行分析。【方法】利用淘宝商品分类体系以及自建的商品词表,根据商品检索的检索式进行购物任务识别,数据集为2 754个用户的19 704个检索会话。【结果】影响每个购物任务所用检索式数的因素包括商品分面、数量的多少以及描述难易程度;有主要任务和次要任务之分的多任务会话中,任务之间的关系更为紧密。【局限】购物任务识别方法有待完善,只以检索式作为研究对象无法全面反映用户行为特征。【结论】本研究可以帮助理解购物中的商品检索行为,并为设计更好的商品推荐算法、预测用户购物过程、行为等提供依据。更多还原

【Abstract】 [Objective] This research aims to identify shopping tasks from product search, and then analyze the characteristic of multi-task sessions. [Methods] Using the product classification of Taobao, and a list of manually selected product terms, we identified online shopping tasks based on query terms from 19 704 search sessions by 2 754 users. [Results] First, factors influence the number of queries per shopping task: product characteristics, the amount of available products, and the difficulty in describing product category with query terms. Second, we found that in sessions with a major task, the relationship among the shopping tasks is closer. [Limitations] The task identification method based on query terms cannot completely describe the complex consumer shopping behaviors. [Conclusions] This study provides an exploratory understanding of the relationships among various shopping tasks, and may be used to improve product recommendation algorithm, as well as predict shopping behaviors.更多还原