Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction

Wang, S; Hu, L; Wang, Y; Sheng, QZ; Orgun, M; Cao, L

Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction

Wang, S

Hu, L Wang, Y Sheng, QZ Orgun, M Cao, L

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Publisher:: Association for the Advancement of Artificial Intelligence (AAAI)
Publication Type:: Conference Proceeding
Citation:: Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34, (04), pp. 6259-6266
Issue Date:: 2020-04-03

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Field	Value	Language
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Hu, L
dc.contributor.author	Wang, Y
dc.contributor.author	Sheng, QZ
dc.contributor.author	Orgun, M
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429
dc.date.accessioned	2021-04-01T11:15:40Z
dc.date.available	2021-04-01T11:15:40Z
dc.date.issued	2020-04-03
dc.identifier.citation	Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34, (04), pp. 6259-6266
dc.identifier.issn	2159-5399
dc.identifier.issn	2374-3468
dc.identifier.uri	http://hdl.handle.net/10453/147806
dc.description.abstract	<jats:p>Human behaviors are complex, which are often observed as a sequence of heterogeneous actions. In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. Most of existing approaches often model user behaviors in a mechanical way, namely treating a user action sequence as homogeneous sequential data, such as hourly temperatures, which fails to consider the complexity in user behaviors. In fact, users' choices are driven by certain underlying intentions (e.g., feeding the baby or relieving pain) according to Psychological theories. Moreover, the durations of intentions to drive user actions are quite different; some of them may be persistent while others may be transient. According to Psychological theories, we develop a hierarchical framework to describe the goal, intentions and action sequences, based on which, we design Intention Nets (IntNet). In IntNet, multiple Action Chain Nets are constructed to model the user actions driven by different intentions, and a specially designed Persistent-Transient Intention Unit models the different intention durations. We apply the IntNet to next-basket prediction, a recent challenging task in recommender systems. Extensive experiments on real-world datasets show the superiority of our Psychology-inspired model IntNet over the state-of-the-art approaches.</jats:p>
dc.language	en
dc.publisher	Association for the Advancement of Artificial Intelligence (AAAI)
dc.relation.ispartof	Proceedings of the AAAI Conference on Artificial Intelligence
dc.relation.isbasedon	10.1609/aaai.v34i04.6093
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction
dc.type	Conference Proceeding
utslib.citation.volume	34
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	open_access	*
dc.date.updated	2021-04-01T11:15:37Z
pubs.issue	04
pubs.publication-status	Published online
pubs.volume	34
utslib.citation.issue	04

Abstract:

Human behaviors are complex, which are often observed as a sequence of heterogeneous actions. In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. Most of existing approaches often model user behaviors in a mechanical way, namely treating a user action sequence as homogeneous sequential data, such as hourly temperatures, which fails to consider the complexity in user behaviors. In fact, users' choices are driven by certain underlying intentions (e.g., feeding the baby or relieving pain) according to Psychological theories. Moreover, the durations of intentions to drive user actions are quite different; some of them may be persistent while others may be transient. According to Psychological theories, we develop a hierarchical framework to describe the goal, intentions and action sequences, based on which, we design Intention Nets (IntNet). In IntNet, multiple Action Chain Nets are constructed to model the user actions driven by different intentions, and a specially designed Persistent-Transient Intention Unit models the different intention durations. We apply the IntNet to next-basket prediction, a recent challenging task in recommender systems. Extensive experiments on real-world datasets show the superiority of our Psychology-inspired model IntNet over the state-of-the-art approaches.

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http://hdl.handle.net/10453/147806