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Learning Location Correlation From GPS Trajectories

Learning Location Correlation From GPS Trajectories. Yu Zheng Microsoft Research Asia March 16, 2010. Background. Locations are correlated in the space of human behavior These location might not belong to the same business categories They would not be co-located. Jewel shop C.

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Learning Location Correlation From GPS Trajectories

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  1. Learning Location Correlation From GPS Trajectories Yu Zheng Microsoft Research Asia March 16, 2010

  2. Background • Locations are correlated in the space of human behavior • These location might not belong to the same business categories • They would not be co-located Jewel shop C Jewel shop B Far away Far away Far away Cafe Cinema Different categories Jewel shop A

  3. What We Do • Mine the correlation between locations from GPS trajectories • The relation between locations in the space of human behavior • Enable a location recommendation system

  4. Challenges • The correlation between locations depends on • Sequence between locations being visited • The travel experience (knowledge) of a user accessing these locations Could be random access ≠ e.g., One-way, accessibility Tourist Cor(A, B)>Cor(A, C)>Cor(A,D) Local expert CorExpert(A, B)>CorTourist(A, B)

  5. Methodology Modeling human location history Inferring user experiences Computing location correlation Personalized location recommender

  6. Solution – Step 1:Modeling human location history • GPS logs P and GPS trajectory • Stay points S={s1, s2,…, sn}. • Stands for a geo-region where a user has stayed for a while • Carry a semantic meaning beyond a raw GPS point • Location history: • represented by a sequence of stay points • with transition intervals

  7. 1. Stay point detection 2. Hierarchical clustering 3.Graph Building

  8. Solution – 2. Infer a user’s experience • Mutual reinforcement relationship • A user with rich travel knowledge are more likely to visit more interesting locations • A interesting location would be accessed by many users with rich travel knowledge • A HITS-based inference model • Users are hub nodes • Locations are authority nodes • Topic is the geo-region

  9. Users: Hub nodes The HITS-based inference model Locations: Authority nodes

  10. Solution –3.Mining the location correlation • The correlation between locations can be represented by the sum of the experiences of the users taking this sequence Trip 1: Trip 2: Trip 3:

  11. Personalized Recommendation • Integrate the location correlation into a CF model • User-location matrix • Slope-One: an item-based CF model Slope-One model Our method

  12. Experimental Settings • 60 Devices and 136 users • From May 2007 ~ present

  13. A large-scale GPS dataset (by Feb. 18, 2009) • 10+ million GPS points • 260+ million kilometers • 36 cities in China and a few city in the USA, Korea and Japan

  14. Results • Effectiveness • Perform a user study-based evaluation • Metric: NDCG & MAP • More effective than the slop-one-based method • Same performance with the Pearson correlation-based CF

  15. Results • Efficiency • Faster than the Pearson-based one • Almost have the same efficiency as the slop one

  16. Conclusion • The correlation between locations in the space of human behavior • Sequence property • User experience • Conduct a personalized location recommender based on the correlation • The recommender is • Efficient than the Pearson correlation-based method and • Effective than the slop one based approach

  17. Thanks! yuzheng@microsoft.com

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