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Found 4 papers, 1 papers with code
Cross-channel Recommendation for Multi-channel Retail
(2) Accordingly, we propose a novel model for CCRR using a channel-wise attention mechanism to capture different user preferences for the same item on each channel.
Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention
To address these gaps, we propose a sequential recommendation model called TemProxRec, which includes contrastive learning and self-attention methods to consider temporal proximities both across and within user-item interactions.
Reward Dropout Improves Control: Bi-objective Perspective on Reinforced LM
We study the theoretical aspects of Reinforced Language Models (RLMs) from a bi-objective optimization perspective.
LayerAct: Advanced activation mechanism utilizing layer-direction normalization for CNNs with BatchNorm
In this work, we propose a novel activation mechanism aimed at establishing layer-level activation (LayerAct) functions for CNNs with BatchNorm.
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