Abstract
The user’s interactions with the system within a given time frame are organized into a session. The task of session-aware recommendation aims to predict the next interaction based on user’s historical sessions and current session. Though existing methods have achieved promising results, they still have drawbacks in some aspects. First, most existing deep learning methods model a session as a sequence, but neglect the complex transition relationships between items. Second, a single account is usually regarded as a single user by default, where the scenario of multiple users sharing the same account is ignored. To this end, we propose a Multi-user Identification network named MISS for the Shared-account Session-aware recommendation problem. MISS consists of two core components: one is the Dwell Graph Neural Network (DGNN), which incorporates item dwell time into the gated graph neural network to capture user interest drift across sessions. The other is a Multi-user Identification (MI) module, which draws on the attention mechanism to distinguish behaviors of different users under the same account. To verify the effectiveness of MISS, we construct two data sets with shared account characteristics from real-world smart TV watching logs. Extensive experiments conducted on the two data sets demonstrate that MISS evidently outperforms the state-of-the-art recommendation methods.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (No. 62072282), Industrial Internet Innovation and Development Project in 2019 of China, Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) (No. 2019JZZY010105). This work is also supported in part by US NSF under Grants III-1763325, III-1909323, and SaTC-1930941.
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Wen, X., Peng, Z., Huang, S., Wang, S., Yu, P.S. (2021). MISS: A Multi-user Identification Network for Shared-Account Session-Aware Recommendation. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12683. Springer, Cham. https://doi.org/10.1007/978-3-030-73200-4_15
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DOI: https://doi.org/10.1007/978-3-030-73200-4_15
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