Open AccessIdentification and Analysis of Multi-tasking Product Information Search Sessions with Query Logs
Author(s) -
Xiang Zhou,
Pengyi Zhang,
Jun Wang
Publication year - 2016
Publication title -
journal of data and information science/journal of data and information science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 8
eISSN - 2543-683X
pISSN - 2096-157X
DOI - 10.20309/jdis.201621
Subject(s) - session (web analytics) , jaccard index , computer science , task (project management) , information retrieval , identification (biology) , originality , product (mathematics) , cluster analysis , search engine indexing , data mining , world wide web , machine learning , mathematics , engineering , botany , geometry , creativity , law , political science , biology , systems engineering
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.