Towards personalized maps : mining user preferences from geo-textual data

Towards personalized maps : mining user preferences from geo-textual data

Rich geo-textual data is available online and the data keeps increasing at a high speed. We propose two user behavior models to learn several types of user preferences from geo-textual data, and a prototype system on top of the user preference models for mining and search geo-textual data (called Pr...

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Bibliographic Details
Main Authors: Zhao, Kaiqi, Liu, Yiding, Yuan, Quan, Chen, Lisi, Chen, Zhida, Cong, Gao
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2019
Subjects:
Engineering::Computer science and engineering
POI Recommendations
Geo-textual Data
Online Access:https://hdl.handle.net/10356/105714
http://hdl.handle.net/10220/49547
http://dx.doi.org/10.14778/3007263.3007305
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Institution: Nanyang Technological University
Language: English
Description
Summary:Rich geo-textual data is available online and the data keeps increasing at a high speed. We propose two user behavior models to learn several types of user preferences from geo-textual data, and a prototype system on top of the user preference models for mining and search geo-textual data (called PreMiner) to support personalized maps. Different from existing recommender systems and data analysis systems, PreMiner highly personalizes user experience on maps and supports several applications, including user mobility & interests mining, opinion mining in regions, user recommendation, point-of-interest recommendation, and querying and subscribing on geo-textual data.

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