• Corpus ID: 17365496

STEPS: Predicting place attributes via spatio-temporal analysis

@article{Nie2016STEPSPP,
  title={STEPS: Predicting place attributes via spatio-temporal analysis},
  author={Shuxin Nie and Abhimanyu Das and Evgeniy Gabrilovich and Wei-Lwun Lu and Boris Mazniker and Chris Schilling},
  journal={ArXiv},
  year={2016},
  volume={abs/1610.07090}
}
In recent years, a vast amount of research has been conducted on learning people's interests from their actions. Yet their collective actions also allow us to learn something about the world, in particular, infer attributes of places people visit or interact with. Imagine classifying whether a hotel has a gym or a swimming pool, or whether a restaurant has a romantic atmosphere without ever asking its patrons. Algorithms we present can do just that. Many web applications rely on knowing… 

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References

SHOWING 1-10 OF 24 REFERENCES

You Are Where You Go: Inferring Demographic Attributes from Location Check-ins

This paper collects a large dataset consisting of profiles of 159,530 verified users from an online social network and proposes a simple yet general location to profile (L2P) framework, which substantially outperforms compared models for profile inference in terms of various evaluation metrics.

Collaborative location and activity recommendations with GPS history data

This paper shows that, by using the location data based on GPS and users' comments at various locations, it can discover interesting locations and possible activities that can be performed there for recommendations and extensively evaluated the system.

What's Your Next Move: User Activity Prediction in Location-based Social Networks

This paper proposes a framework which uses a mixed hidden Markov model to predict the category of user activity at the next step and then predict the most likely location given the estimated category distribution.

Mining correlation between locations using human location history

By taking into account a user's travel experience and the sequentiality locations have been visited, an approach to mine the correlation between locations from a large number of users' location histories is proposed.

Where You Like to Go Next: Successive Point-of-Interest Recommendation

This paper proposes a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions in the check-in sequence, and utilizes the information of localized regions to boost recommendation.

Exploiting geographical influence for collaborative point-of-interest recommendation

This paper argues that the geographical influence among POIs plays an important role in user check-in behaviors and model it by power law distribution, and develops a collaborative recommendation algorithm based on geographical influence based on naive Bayesian.

GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation

The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon in human mobility behavior on the LBSNs into matrixfactorization improves recommendation performance.

Inferring Movement Trajectories from GPS Snippets

This paper presents a complete and computationally tractable model for estimating and predicting trajectories based on sparsely sampled, anonymous GPS land-marks that they call GPS snippets, and uses mapping data as side information to constrain the inference process.

Location-Based Activity Recognition

This work shows how to extract and label a person's activities and significant places from traces of GPS data and applies FFT-based message passing to perform efficient summation over large numbers of nodes in the networks.

Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields

This paper model the problem as an information extraction task, which is addressed based on Conditional Random Fields (CRF), and employs the supervised algorithm by Zhuang et al. (2006), which represents the state-of-the-art on the employed data.