Paper Info
Title
A Collaborative Learning Framework to Tag Refinement for Points of Interest
Abstract
Tags of a Point of Interest (POI) can facilitate location-based services from many aspects like location search and place recommendation. However, many POI tags are often incomplete or imprecise, which may lead to performance degradation of tag-dependent applications. In this paper, we study the POI tag refinement problem which aims to automatically fill in the missing tags as well as correct noisy tags for POIs. We propose a tri-adaptive collaborative learning framework to search for an optimal POI-tag score matrix. The framework integrates three components to collaboratively (i) model the similarity matching between POI and tag, (ii) recover the POI-tag pattern via matrix factorization and (iii) learn to infer the most possible tags by maximum likelihood estimation. We devise an adaptively joint training process to optimize the model and regularize each component simultaneously. And the final refinement results are the consensus of multiple views from different components. We also discuss how to utilize various data sources to construct features for tag refinement, including user profile data, query data on Baidu Maps and basic properties of POIs. Finally, we conduct extensive experiments to demonstrate the effectiveness of our framework. And we further present a case study of the deployment of our framework on Baidu Maps.
Year
DOI
Keywords
2019
10.1145/3292500.3330698
collaborative learning, location based service, point of interest, tag mining, tag refinement
Field
DocType
ISSN
Data science,Collaborative learning,Computer science,Artificial intelligence,Point of interest,Machine learning
Conference
978-1-4503-6201-6
ISBN
Citations 
PageRank 
978-1-4503-6201-6
7
0.50
References 
Authors
0
8
Name
Order
Citations
PageRank
Jingbo Zhou19422.78
Shan Gou270.50
Renjun Hu3365.65
Dongxiang Zhang474343.89
Jin Xu570.50
Airong Jiang670.50
Ying Li7111.23
Hui Xiong84958290.62