Urban Point-of-Interest Recommendation by Mining User Check-in Behaviors

1. Urban Point-of-Interest Recommendation by Mining User Check-in Behaviors Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wen-Ning Kuo and Vincent S. Tseng Institute of Computer Science and Information Engineering National Cheng Kung University No.1, University Road, Tainan City 701, Taiwan (R.O.C.)

2. Outline • Introduction • Background • Motivation • Challenges • Proposed Method –UPOI-Mine • Experimental Results • Conclusions

3. Introduction–Background • The markets of Location-Based Services (LBSs) in urban areas have grown rapidly. • Effective and efficient urban POI recommendation techniques are desirable. • Location Based Social Network (LBSN) data is widely used for building POI recommendation model. 3

4. Introduction–Background (cont.) • heterogeneous data 4

5. Introduction–Motivation We can not accurately catch users’ preference by analyzing his and his friend’s check-in actives ? ? 5

6. Introduction–Challenges • How to understand user preference from LBSN data? • How to extract useful features from heterogeneous data? • How to precisely estimate the relevance between a user-POI pair based on the extracted features? • How to integrate heterogeneous information? 6

7. Proposed Method – UPOI-Mine Offline :UPOI-Mine User-to-POI Relevance Estimation Individual Preference (IP) POI popularity (PP) Social Factor (SF) Location Types Check-in Data Social Links User-to-POI Relevance Matrix LBSN Dataset Online: Recommender Recommendation Model User Request POIs Recommendation List 7

8. Feature Extraction Offline :UPOI-Mine User-to-POI Relevance Estimation Individual Preference (IP) POI popularity (PP) Social Factor (SF) Location Types Check-in Data Social Links User-to-POI Relevance Matrix LBSN Dataset Online: Recommender Recommendation Model User Request POIs Recommendation List 8

9. Social Factor (SF) Weighted summation: Weight F: friends of user i S: the set of POIs U: the set of user i’s friends Check-in k,* = check-ins of user k at POI* 9

10. Social Factor – Relation • Check-in Similarity (CheckSim) • based on their check-in log • Relative Distance Similarity (DisSim) • based on their geographic distance 10

11. Relation – CheckSim Friend Indicator 11

12. Relation – DisSim Distance Friend Indicator Distance  dissimilarity Maxi=1000 12

13. Social Factor – Example Relation: CheckSim(A, B) = 0.5 DisSim(A, B) = 0.03 User A ? POIk User B #Check-ins at POIK : 10 #Total Check-ins : 100 Interest(B,POIK) = 13

14. Individual Preference (IP) highlight category • Individual Preference(IP) • HPrefi,h • CPrefi,c 14

15. Individual Preference – HPref & CPref proportion of check-ins of the label 15

16. Individual Preference – Example User A’s pref table • There is only one category for one POI. • There are many highlights for one POI. Counts of highlight POI Category: Hotdog & Sausages Highlight: Coffee(12), Cheese(88) 16

17. Individual Preference – Example (cont.) User A’s pref table POI A Category: Hotdog & Sausages Highlight: Coffee(12), Cheese(88) CPref HPref 17

18. POI Popularity (PP) • POI Popularity • Relative Popularity of POI • Normalized based on category 18

19. POI Popularity – Example Frank Category: Hot Dogs 19

20. Relevance Estimation Offline :UPOI-Mine User-to-POI Relevance Estimation Individual Preference (IP) POI popularity (PP) Social Factor (SF) Location Types Check-in Data Social Links User-to-POI Relevance Matrix LBSN Dataset Online: Recommender Recommendation Model User Request POIs Recommendation List 20

21. Relevance Estimation – Example To estimate the relevance of each pair of user to POI, we use these feature to learn a Regression-Tree Model. Target Regression-Tree Model 21

22. Relevance Estimation –Regression-Tree Model • Regression-Tree Model has shown excellent performance for numerical value prediction • Demographic Prediction • Bio Life Cycle Analysis • Prediction of Geographical Natural • Learning Steps: • 1. Building the initial tree • 2. Linear regression model for each leaf node • 3. Pruning the tree 22

23. Recommender Offline :UPOI-Mine User-to-POI Relevance Estimation Individual Preference (IP) POI popularity (PP) Social Factor (SF) Location Types Check-in Data Social Links User-to-POI Relevance Matrix LBSN Dataset Online: Recommender Recommendation Model User Request POIs Recommendation List 23

24. Experimental Evaluation • Experimental dataset – Gowalla Dataset • Near or within New York City • 1,964,919 POIs • 18,159 people • 5,341,191 Check-ins • 392,246 Friendship Links 24

25. Experimental Evaluation • Experimental measurements • Normalized Discounted Cumulative Gain (NDCG) • To measure ranking performance of relevance score of top k POIs in recommendation list • Mean Absolute Error (MAE) • To measure error of relevance score of all POIs 25

26. Experimental Evaluation (cont.) avg = 200 • Ground Truth • Baseline • Trust Walker • M. Jamali, M. Ester. TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation. Proceedings of KDD, pages 397-406, Paris, 2009. • Multi-Factor CF-based • M. Ye, P. Yin, W.-C. Lee and Dik-Lun Lee. Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation. Proceedings of SIGIR, pages 1046-1054, Beijing, China, 2011. 26

27. Comparison of Various Features • The Individual Preference is more important than Social Factor for urban POI recommendation. 27

28. Comparison of Various Features (cont.) 28

29. Comparison with Existing Recommenders 29

30. Comparison with Existing Recommenders (cont.) 30

31. Conclusions • We proposed a novel urban POIsrecommendation which is called UPOI-Mine by mining users’ preferences. • we propose three kinds of useful features • Social Factor • Individual Preference • POI Popularity • Through a series of experiments by the real dataset Gowalla • We have validated our proposed UPOI-Mine and shown that UPOI-Mine has excellent performance under various conditions. • The Individual Preference is more important than Social Factor for urban POI recommendation.

32. Question? Thank you for your attentions