Learning Location Correlation From GPS Trajectories

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