主要解决的问题：给POI-tag的关系打分。The tag refinement problem.
本文可以学习和借鉴的地方：POI, tag features 学习和设定方法。在之后有关POI研究当中可以借鉴
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| A Collaborative Learning Framework to Tag Refinement for Points of Interest | |||||||||||
| 文献作者 | Jingbo Zhou; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| Tag refinement | |||||||||||
| 摘要描述 | |||||||||||
| 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. | |||||||||||
