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| Multi-task Session Identification and Analysis in Product Search |
Zhang Pengyi,Zhou Xiang( ),Wang Jun |
| Department of Information Management, Peking University, Beijing 100871, China |
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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.
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Received: 19 October 2015
Published: 12 April 2016
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