<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis.201621_s_006_w2aab2b8c68b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>This research aims to identify product search tasks in online shopping and analyze the characteristics of consumer multi-tasking search sessions.</p></sec><sec id="j_jdis.201621_s_007_w2aab2b8c68b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>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.</p></sec><sec id="j_jdis.201621_s_008_w2aab2b8c68b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>(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.</p></sec><sec id="j_jdis.201621_s_009_w2aab2b8c68b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>The task identification method that relies only on query terms does not completely reflect the complex nature of consumer shopping behavior.</p></sec><sec id="j_jdis.201621_s_010_w2aab2b8c68b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>These results provide an exploratory understanding of the relationships among multiple shopping tasks, and can be useful for product recommendation and shopping task prediction.</p></sec><sec id="j_jdis.201621_s_011_w2aab2b8c68b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>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.</p></sec></abstract>

PurposeThis 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.

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

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

PurposeThis research aims to identify product search tasks in online shopping and analyze the characteristics of consumer multi-tasking search...

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

Article Category: Research Paper

Online veröffentlicht: 01. Sept. 2017

Seitenbereich: 79 - 94

Eingereicht: 16. März 2016

Akzeptiert: 06. Juni 2016

DOI: https://doi.org/10.20309/jdis.201621

Schlüsselwörter
Product search, Shopping task identification, Shopping task analysis, Multitasking session

© 2016 Xiang Zhou, Pengyi Zhang, Jun Wang

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Article Category: Research Paper

Online veröffentlicht: 01. Sept. 2017

Seitenbereich: 79 - 94

Eingereicht: 16. März 2016

Akzeptiert: 06. Juni 2016

DOI: https://doi.org/10.20309/jdis.201621

SchlüsselwörterProduct search, Shopping task identification, Shopping task analysis, Multitasking session

© 2016 Xiang Zhou, Pengyi Zhang, Jun Wang

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Product search, Shopping task identification, Shopping task analysis, Multitasking session