Identification and Analysis of Multi-tasking Product Information Search Sessions with Query Logs

• 作者: Jun Wang Pengyi Zhang Xiang Zhou
• 来源期刊: 数据与情报科学学报：英文版
• 学科: 社会科学
• 年，卷（期）: 2016,（3）
• 页码范围: 79-94
• 页数: 16页
• 分类号: G2

摘要

Purpose: This research aims to identify product search tasks in online shopping and analyze the characteristics of consumer multi-tasking search sessions.Design/methodology/approach: The experimental dataset contains 8,949 queries of 582 users from 3,483 search sessions. A sequential comparison of the Jaccard similarity coefficient between two adjacent search queries and hierarchical clustering of queries is used to identify search tasks.Findings:(1) Users issued a similar number of queries(1.43 to 1.47) with similar lengths(7.3–7.6 characters) per task in mono-tasking and multi-tasking sessions, and(2) Users spent more time on average in sessions with more tasks, but spent less time for each task when the number of tasks increased in a session.Research limitations: The task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior.Practical implications: These results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction.Originality/value: The originality of this research is its use of query clustering with online shopping task identification and analysis, and the analysis of product search session characteristics.