In Foursquare or Google+ Local, emerging spatial entities, such as new business or venue, are reported to grow by 1% every day. As information on such spatial entities is initially limited (e.g., only name), we need to quickly harvest related information from social media such as Flickr photos. Especially, achieving high-recall in photo population is essential for emerging spatial entities, which suffer from data sparseness (e.g., 71% restaurants of TripAdvisor in Seattle do not have any photo, as of Sep 03, 2015). Our goal is thus to address this limitation by identifying effective linking techniques for emerging spatial entities and photos. Compared with state-of-the-art baselines, our proposed approach improves recall and F1 score by up to 24% and 18%, respectively. To show the effectiveness and robustness of our approach, we have conducted extensive experiments in three different cities, Seattle,Washington D.C., and Taipei, of varying characteristics such as geographical density and language.
|Title of host publication||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Number of pages||7|
|Publication status||Published - 2016|
|Event||30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States|
Duration: 2016 Feb 12 → 2016 Feb 17
|Name||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Other||30th AAAI Conference on Artificial Intelligence, AAAI 2016|
|Period||16/2/12 → 16/2/17|
Bibliographical notePublisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence