AbstractPurposeThis research aims to identify product search tasks in online shopping and analyze the characteristics of consumer multi-tasking search sessions.Design/methodology/approachThe 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 limitationsThe task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior.Practical implicationsThese results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction.Originality/valueThe 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.
Dynamic Graph Modeling Of Simultaneous EEG And Eye-Tracking Data For Reading Task Identification
