Search Results for author: Gao Cong

Found 14 papers, 4 papers with code

Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks

no code implementations28 Feb 2022 Yile Chen, Xiucheng Li, Gao Cong, Cheng Long, Zhifeng Bao, Shang Liu, Wanli Gu, Fuzheng Zhang

As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers.

Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation

1 code implementation13 Sep 2021 Yuxing Han, Ziniu Wu, Peizhi Wu, Rong Zhu, Jingyi Yang, Liang Wei Tan, Kai Zeng, Gao Cong, Yanzhao Qin, Andreas Pfadler, Zhengping Qian, Jingren Zhou, Jiangneng Li, Bin Cui

Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods.

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

1 code implementation9 Jun 2021 Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, Minghui Qiu

In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph.

Representation Learning Session-Based Recommendations

A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments

no code implementations8 Mar 2021 Tu Gu, Kaiyu Feng, Gao Cong, Cheng Long, Zheng Wang, Sheng Wang

Learned indices have been proposed to replace classic index structures like B-Tree with machine learning (ML) models.


Exploring Global Information for Session-based Recommendation

no code implementations20 Nov 2020 Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, Minghui Qiu, Shanshan Feng

Based on BGNN, we propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences via fully exploring global item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of session level item representation; and (ii) Constrained-based Model (SRGI-CM), which treats the global-level item-transition information as a constraint to ensure the learned item embeddings are consistent with the global item-transition.

Session-Based Recommendations

Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning

no code implementations5 Mar 2020 Zheng Wang, Cheng Long, Gao Cong, Yiding Liu

Similar trajectory search is a fundamental problem and has been well studied over the past two decades.


Interaction-aware Kalman Neural Networks for Trajectory Prediction

no code implementations28 Feb 2019 Ce Ju, Zheng Wang, Cheng Long, Xiao-Yu Zhang, Gao Cong, Dong Eui Chang

Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.)

Robotics I.2.9; I.2.0

Representation Learning for Spatial Graphs

no code implementations17 Dec 2018 Zheng Wang, Ce Ju, Gao Cong, Cheng Long

Recently, the topic of graph representation learning has received plenty of attention.

Denoising Graph Representation Learning

Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation

no code implementations12 Apr 2018 Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay, Yiding Liu, Gao Cong, Xiao-Li Li

Our proposed approach hinges upon the key intuition that the decision making process (in groups) is generally dynamic, i. e., a user's decision is highly dependent on the other group members.

Decision Making

Heron Inference for Bayesian Graphical Models

1 code implementation19 Feb 2018 Daniel Rugeles, Zhen Hai, Gao Cong, Manoranjan Dash

Bayesian graphical models have been shown to be a powerful tool for discovering uncertainty and causal structure from real-world data in many application fields.

Variational Inference

Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation

1 code implementation1 Aug 2015 Xutao Li, Gao Cong, Xiaoli Li, Tuan Anh Nguyen Pham, Shonali Priyadarsini Krishnaswamy

In this paper, we propose a ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges.

Recommendation Systems

Graph-based Point-of-interest Recommendation with Geographical and Temporal Influences

no code implementations1 Nov 2014 Quan Yuan, Gao Cong, Aixin Sun

In this paper, we focus on the problem of time-aware POI recommendation, which aims at recommending a list of POIs for a user to visit at a given time.

Recommendation Systems

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