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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.
Original language | English |
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Title of host publication | The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) |
Place of Publication | Palo Alto, CA |
Publisher | Association for the Advancement of Artificial Intelligence |
Pages | 6259-6266 |
Number of pages | 8 |
ISBN (Print) | 9781577358350 |
DOIs | |
Publication status | Published - 2020 |
Event | AAAI Conference on Artificial Intelligence (34th : 2020) - New York, United States Duration: 7 Feb 2020 → 12 Feb 2020 |
Conference
Conference | AAAI Conference on Artificial Intelligence (34th : 2020) |
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Abbreviated title | AAAI 20 |
Country | United States |
City | New York |
Period | 7/02/20 → 12/02/20 |
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Projects
- 1 Finished
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Reputation-based Trust Management in Crowdsourcing Environments
Wang, Y., Sheng, M., Orgun, M., MQRES (International), M. (. & MQRES, M.
1/01/18 → 31/12/20
Project: Research