Name
Abstract | ||
|---|---|---|
Location-based social media (e.g., Twitter, Foursquare) have been generating massive amount of geo-textual data. In this paper, we represent the spatial distribution of a keyword by the group of locations tagged with such keyword. Given a query keyword, our problem is to find k keywords with the most similar distribution of locations. Such query finds applications in targeted marketing and recommendation. The performance of existing solutions degrade when different point groups have significant overlapping, which happens rather frequently in real data. We propose efficient techniques to process similarity search on point groups. Experimental results on Twitter data demonstrate that our solution is faster than the state-of-the-art by up to 6 times.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3274895.3274920 | SIGSPATIAL/GIS |
Keywords | Field | DocType |
Hausdorff distance, Similarity Searching, Spatio-Textual Searching | Data mining,Social media,Computer science,Point group,Targeted marketing,Hausdorff distance,Nearest neighbor search | Conference |
ISBN | Citations | PageRank |
978-1-4503-5889-7 | 0 | 0.34 |
References | Authors | |
20 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Li | 1 | 30 | 16.58 |
| Yu Li | 2 | 25 | 4.17 |
| man lung yiu | 3 | 2436 | 109.78 |