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- W3203195851 abstract "Session-based recommendation aims to suggest items to users mainly by modeling sequential dependencies. However, existing methods mainly focus on resorting either temporal interests with time-series data or inherent interests by leveraging static user behaviors, ignoring benefits of the dynamic aggregation from short- and long-term preferences, resulting in unfavorable performance in various sequence recommendation scenarios. In this paper, we attempt to combine users' short interests with long preferences synthetically, and tackle the sequential recommendation problem from new views of the causal (short) and non-causal (long) perspectives. We propose a dynamic gap-complementary generative framework named DGRec for session-based Recommendation. Specifically, we fuse two practical neural network models: the dilated convolutional neural network (DCNN) and the self-attention block (SAB). DCNN mainly captures users' short-term interests while SAB is responsible for obtaining users' long-term inherent behaviors. Besides, we devise a gap-complementary strategy to dynamically learn item sequence representation, effectively enhancing the robustness and anti-interference ability of model. Extensive experimental results over five public datasets demonstrate that DGRec gains significant improvements against the advanced sequential recommendation methods." @default.
- W3203195851 created "2021-10-11" @default.
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- W3203195851 date "2021-07-18" @default.
- W3203195851 modified "2023-09-26" @default.
- W3203195851 title "Causal and Non-causal Training: A Dynamic Gap-complementary Generative Framework for Session-based Recommendation" @default.
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- W3203195851 doi "https://doi.org/10.1109/ijcnn52387.2021.9534230" @default.
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