Geo-activity Recommendations by using Improved Feature Combination

1. Geo-activity Recommendations by using Improved Feature Combination Masoud Sattari, Ismail H. Toroslu, Pinar Senkul, Murat Manguoglu Panagiotis Symeonidis*, Yannis Manolopoulos* Middle East Technical University (METU), Turkey *Aristotle University of Thessaloniki, Greece LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

2. Introduction • Increasing role of GPS-assisted systems in daily life • Recommendation based on geographical position of user LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

3. Problem Definition • Recommend an activity to a user that is in a location • Recommend a location for a user that wants to do a specific activity • Data set is very sparse • System should be able to predict the values of missing entries LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

4. Data set • Data set gathered by Microsoft Research Asia • GPS trajectory of 162 users in 2.5 years • Add comments about activities done at a specific location • Data is organized in different Matrices • Yellow books of cities give informative data • Number of available Features in a specific areas of a city (Sport, Food, Shopping, Music,…) LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

5. Data set (cont.) LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

6. Our method • We merge additional data to original data set • Singular Value Decomposition (SVD) is a common method to reveal latent semantic structure • Use SVD to propagate the effect of additional data LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

7. Merging model LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

8. Prediction on Similar rows and columns LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

9. Example LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

10. Example (cont.) LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

11. Example (cont.) LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

12. Example (cont.) LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

13. Example (con.) LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

14. Evaluation method LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

15. Experimental Results Finding optimal parameters: • Selecting the number of top similar rows and columns affects final results directly. • Parameters m and n should be selected so that, MAE and RMSE be as low as possible. • To find optimal value of m, different values of n are examined to get minimum RMSE. Same method is used to find optimal value of m LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

16. Experimental Results Optimal m=1without abstraction LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

17. Experimental Results • In each fold of execution 10% of matrix X is set to 0 randomly and these entries are estimated with our method. • Zheng, V. W., Zheng, Y., Xie, X. and Yang, Q. Collaborative location and activity recommendations with GPS history data. In WWW ’10: Proc. of the 19th InternationalWorld Wide Web Conference. New York, NY, USA: ACM. LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

18. Experimental Results • Prediction with Abstraction: • In the Location-Activity matrix, interval between maximum and minimum values is large • Some values are very large, in the range of a few hundreds, there are also so many values less than 100. • Rather than using these actual values, it would be better if abstract and discrete values are used in a small range • Partition the nonzero values of Location-Activity matrix to clusters using k-means clustering ( k=5 for our data set) • Ex: C1=1,1,2,3,4, C2=17,17C3=53,53C4=76,82 • Find the error between the clusters that original value belongs to and the cluster that predicted value falls into it. LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

19. Experimental Results Optimal m=4 with abstraction LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

20. Experimental Results • An abstraction method also is applied to both methods and error terms are as following diagrams. • Zheng, V. W., Zheng, Y., Xie, X. and Yang, Q. Collaborative location and activity recommendations with GPS history data. In WWW ’10: Proc. of the 19th InternationalWorld Wide Web Conference. New York, NY, USA: ACM. LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

21. Conclusion • Merging metrices + SVN • An abstraction technique is performed to evaluate results fairly • Optimized parameter for similarity • Final results especially in RMSE reveal improvement on prediction • Future work: Integrate user into this schema LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8

22. The End Thanks for your attention! Any question? LBSN 2012, Pittsburgh, Pennsylvania, USA Sept 8