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Found 6 papers, 0 papers with code
Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data
To address this issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN), which is able to "deduce" future travel time by exploiting the data of upcoming traffic volume.
LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding
In this work, we propose a knowledge distillation method LRC-BERT based on contrastive learning to fit the output of the intermediate layer from the angular distance aspect, which is not considered by the existing distillation methods.
Denoising User-aware Memory Network for Recommendation
Specifically, the framework: (i) proposes a feature purification module based on orthogonal mapping, which use the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback; (ii) designs a user memory network to model the long-term interests in a fine-grained way by improving the memory network, which is ignored by the existing methods; and (iii) develops a preference-aware interactive representation component to fuse the long-term and short-term interests of users based on gating to understand the evolution of unbiased preferences of users.
R4: A Framework for Route Representation and Route Recommendation
For user representation, we utilize a series of historical navigation to extract user preference.
A General Framework for Debiasing in CTR Prediction
Most of the existing methods for debaising in click-through rate (CTR) prediction depend on an oversimplified assumption, i. e., the click probability is the product of observation probability and relevance probability.
DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking
Multi-scenario route ranking (MSRR) is crucial in many industrial mapping systems.
