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In contrast to conventional studies of discovering hot spots, by analyzing geo-tagged images on Flickr, we introduce novel methods to discover obscure sightseeing spots that are less well-known while still worth visiting. To this end, we face two new challenges that the classical authority analysis based methods do not encounter: how to discover and rank spots on the basis of 1) popularity (obscurity level) and 2) scenery quality. For the first challenge, we estimate the obscurity level of a spot in accordance with the visiting asymmetry between photographers who are familiar with a target city and those who are not. For the second challenge, the behavior of both viewers who browsed the images and photographers are analyzed per each spot. We also develop an application system to help users to explore sightseeing spots with different geographical granularities. Experimental evaluations and analysis on a real dataset well demonstrate the effectiveness of the proposed methods.
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Stored only when both papers are in the corpus — 21 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.