A spatial insight for UGC Apps: Fast similarity search on keyword-induced point groups

Zhe Li, Yu Li, Man Lung Yiu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

In the era of smartphones, massive data are generated with geo-related info. A large portion of them come from UGC applications (e.g., Twitter, Instagram), where the content provider are users themselves. Such applications are highly attractive for targeted marketing and recommendation, which have been well studied in recommendation system. In this paper, we consider this from a brand new spatial aspect using UGC contents only. To do this we first representing each message as a point with its geo info as its location and then grouping all the points by their keywords to form multiple point groups. We form a similarity search problem that given a query keyword, our problem aims to find k keywords with the most similar distribution of locations. Our case study shows that with similar distribution, the keywords are highly likely to have semantic connections. However, the performance of existing solutions degrades when different point groups have significant overlapping, which frequently happens in UGC contents. We propose efficient techniques to process similarity search on this kind of point groups. Experimental results on Twitter data demonstrate that our solution is faster than the state-of-The-Art by up to 6 times.

Original languageEnglish
Title of host publicationProceedings - 2019 20th International Conference on Mobile Data Management, MDM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages371-372
Number of pages2
ISBN (Electronic)9781728133638
DOIs
Publication statusPublished - Jun 2019
Event20th International Conference on Mobile Data Management, MDM 2019 - Hong Kong, Hong Kong
Duration: 10 Jun 201913 Jun 2019

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
Volume2019-June
ISSN (Print)1551-6245

Conference

Conference20th International Conference on Mobile Data Management, MDM 2019
Country/TerritoryHong Kong
CityHong Kong
Period10/06/1913/06/19

Keywords

  • Hausdorff distance
  • Similarity Searching
  • Spatio-Textual Searching

ASJC Scopus subject areas

  • Engineering(all)

Cite this