A Location Recall Strategy for Improving Efficiency of User-Generated Short Text Geolocalization

Abstract

Currently, users’ daily lives are closely related to social sites. Geolocalizing user-generated short texts (UGSTs) can provide services for the wider fields of business and society. The existing works focus on improving the accuracy of geolocalization algorithms and reducing the prediction error. However, little work has been done on improving the computing efficiency of geolocalization algorithms. When dealing with large-scale data, the existing algorithm efficiency is greatly reduced. To solve this problem, we summarize three modes of user access to point of interests (PoIs), further propose a variety of location recall methods, and then introduce these geolocation recall methods into the existing algorithm. Geolocation recall generates a candidate location set for each user according to the user’s historical behavior records to reduce the search space of the geolocalization algorithm and improve its efficiency. We carried out experiments on the ground-truth datasets, compared the performance of various location recall methods, and verified the excellent location recall performance of three geolocalization algorithms. The experimental results show that location recall shortens the calculation time of the three algorithms by approximately 70% and further improves the accuracy of these algorithms.

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