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- W4386730032 abstract "Session-based recommendation (SBR), which makes the next-item recommendation based on previous anonymous actions, has drawn increasing attention. The last decade has seen multiple deep learning-based modeling choices applied on SBR successfully, e.g., recurrent neural networks (RNNs), convolutional neural networks (CNNs), graph neural networks (GNNs), and each modeling choice has its intrinsic superiority and limitation. We argue that these modeling choices differentiate from each other by (1) the way they capture the interactions between items within a session and (2) the operators they adopt for composing the neural network, e.g., convolutional operator or self-attention operator." @default.
- W4386730032 created "2023-09-15" @default.
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- W4386730032 date "2023-09-14" @default.
- W4386730032 modified "2023-09-26" @default.
- W4386730032 title "Multiple Connectivity Views for Session-based Recommendation" @default.
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- W4386730032 doi "https://doi.org/10.1145/3604915.3608861" @default.
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