Travel topic analysis: a mutually reinforcing method for geo-tagged photos

@article{Kou2015TravelTA,
  title={Travel topic analysis: a mutually reinforcing method for geo-tagged photos},
  author={Ngai Meng Kou and Leong Hou U and Y. Yang and Zhiguo Gong},
  journal={GeoInformatica},
  year={2015},
  volume={19},
  pages={693-721}
}
Sharing personal activities on social networks is very popular nowadays, where the activities include updating status, uploading dining photos, sharing video clips, etc. Finding travel interests hidden in these vast social activities is an interesting but challenging problem. In this work, we attempt to discover travel interests based on the spatial and temporal information of geo-tagged photos. Obviously the visit sequence of a traveler can be approximately captured by her shared photos based… 
Point of interest mining with proper semantic annotation
TLDR
Experimental results on two datasets of geo-tagged Flickr photos of two cities in California, USA have shown that the proposed method substantially outperforms existing approaches that are adapted to handle the problem.
Trip Outfits Advisor: Location-Oriented Clothing Recommendation
TLDR
This paper proposes a hybrid multilabel convolutional neural network combined with the support vector machine (mCNN-SVM) approach to capture the intrinsic and complex correlations between clothing attributes and location attributes.
Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs
Spatial patterns of tourist mobility are important for tourism management and planning. A large number of traveler-generated content accumulated on the internet provide a unique opportunity for
Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow
TLDR
The data preprocessing method introduced and designed in this paper provides a reference for the study of geo-tagged photo metadata in the field of tourism flow prediction, and can effectively filter out inbound tourist flow data from geotag photo metadata, thus providing a novel, reliable, and low-cost research data source for urban inbound tourism flow forecasting.
Efficient Processing of the SkyEXP Query Over Big Data
TLDR
In order to fast implement the proposed query over big data, an efficient parallel algorithm SQMRM (the SkyEXP Query using Map-Reduce Model) which utilizes the map-reduce framework is presented.

References

SHOWING 1-10 OF 45 REFERENCES
Mining Travel Patterns from Geotagged Photos
TLDR
This study aims to leverage the wealth of these enriched online photos to analyze people’s travel patterns at the local level of a tour destination by building a statistically reliable database of travel paths from a noisy pool of community-contributed geotagged photos on the Internet.
Travel route recommendation using geotags in photo sharing sites
TLDR
A travel route recommendation method that makes use of the photographers' histories as held by Flickr, and incorporates user preference and present location information into the probabilistic behavior model by combining topic models and Markov models.
Personalized travel recommendation by mining people attributes from community-contributed photos
TLDR
This work proposes to conduct personalized travel recommendation by leveraging the freely available community-contributed photos to leverage the automatically detected people attributes in the photo contents to improve prior travel recommendation methods especially in difficult predictions by further leveraging user contexts in mobile devices.
Personalized Landmark Recommendation Based on Geotags from Photo Sharing Sites
TLDR
The experimental results demonstrate that the proposed approach outperforms popularity-based landmark recommendation and a basic matrix factorization approach in recommending personalized landmarks that are less visited by the population as a whole.
Deducing trip related information from flickr
TLDR
This work deduces answers to questions like "how long does it take to visit a tourist attraction?" or "what can I visit in one day in this city?" by comparing the automatically obtained visit duration times to manual estimations.
Geographical topic discovery and comparison
TLDR
The results confirm the hypothesis that the geographical distributions can help modeling topics, while topics provide important cues to group different geographical regions.
Mining social media to create personalized recommendations for tourist visits
TLDR
This work mine the record of visited landmarks exposed in online user data to build a user-user similarity matrix and compares this recommender to a baseline which simulates classical tourist guides on a large sample of Flickr users.
Identifying points of interest by self-tuning clustering
TLDR
Spectral clustering is studied which is the first attempt for the POIs identification in geo-tagged photos and reinforcement which constructs the relationship over multiple sources by iterative learning is studied.
Mining interesting locations and travel sequences from GPS trajectories
TLDR
This work first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG), and proposes a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location.
Equip tourists with knowledge mined from travelogues
TLDR
This paper proposes a probabilistic topic model, named as Location-Topic model, which has the advantages of differentiability between two kinds of topics, i.e., local topics which characterize locations and global topics which represent other common themes shared by various locations.
...
...