Search Results for author: Kaikui Liu

Found 4 papers, 0 papers with code

R4: A Framework for Route Representation and Route Recommendation

no code implementations20 Oct 2021 Ran Cheng, Chao Chen, Longfei Xu, Shen Li, Lei Wang, Hengbin Cui, Kaikui Liu, Xiaolong Li

For user representation, we utilize a series of historical navigation to extract user preference.

Denoising User-aware Memory Network for Recommendation

no code implementations12 Jul 2021 Zhi Bian, Shaojun Zhou, Hao Fu, Qihong Yang, Zhenqi Sun, Junjie Tang, Guiquan Liu, Kaikui Liu, Xiaolong Li

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.

Denoising

LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding

no code implementations14 Dec 2020 Hao Fu, Shaojun Zhou, Qihong Yang, Junjie Tang, Guiquan Liu, Kaikui Liu, Xiaolong Li

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.

Contrastive Learning Knowledge Distillation +2

Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data

no code implementations23 Jun 2020 Rui Dai, Shenkun Xu, Qian Gu, Chenguang Ji, Kaikui Liu

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.

Traffic Prediction

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