Search Results for author: Zhifeng Bao

Found 7 papers, 2 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.

Spatial Object Recommendation with Hints: When Spatial Granularity Matters

no code implementations8 Jan 2021 Hui Luo, Jingbo Zhou, Zhifeng Bao, Shuangli Li, J. Shane Culpepper, Haochao Ying, Hao liu, Hui Xiong

We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level.

Multi-Task Learning Representation Learning

A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration

no code implementations5 Jan 2021 Hai Lan, Zhifeng Bao, Yuwei Peng

A cost-based optimizer introduces a plan enumeration algorithm to find a (sub)plan, and then uses a cost model to obtain the cost of that plan, and selects the plan with the lowest cost.

On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection

no code implementations13 Oct 2020 Sheng Wang, Yuan Sun, Zhifeng Bao

This paper presents a thorough evaluation of the existing methods that accelerate Lloyd's algorithm for fast k-means clustering.

Temporal Network Representation Learning via Historical Neighborhoods Aggregation

1 code implementation30 Mar 2020 Shixun Huang, Zhifeng Bao, Guoliang Li, Yanghao Zhou, J. Shane Culpepper

More specifically, we first propose a temporal random walk that can identify relevant nodes in historical neighborhoods which have impact on edge formations.

Link Prediction Network Embedding +1

Location-Centered House Price Prediction: A Multi-Task Learning Approach

no code implementations7 Jan 2019 Guangliang Gao, Zhifeng Bao, Jie Cao, A. K. Qin, Timos Sellis, Fellow, IEEE, Zhiang Wu

Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently.

Multi-Task Learning

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