1. Intelligent DataBase System Lab, NCKU, Taiwan 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.) Urban Point-of-Interest Recommendation by Mining User Check-in Behaviors

2. Intelligent DataBase System Lab, NCKU, Taiwan Outline 2 Introduction Background Motivation Challenges Proposed Method – UPOI-Mine Experimental Results Conclusions

3. Intelligent DataBase System Lab, NCKU, Taiwan 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. Intelligent DataBase System Lab, NCKU, Taiwan Introduction – Background (cont.) heterogeneous data 4

5. Intelligent DataBase System Lab, NCKU, Taiwan Introduction – Motivation 5 ? ? We can not accurately catch users’ preference by analyzing his and his friend’s check-in actives

6. Intelligent DataBase System Lab, NCKU, Taiwan Introduction – Challenges 6 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?

7. Intelligent DataBase System Lab, NCKU, Taiwan Proposed Method – UPOI-Mine 7 Online: Recommender Offline :UPOI-Mine LBSN Dataset Location Types Social Links Check-in Data Individual Preference (IP) Social Factor (SF) POI popularity (PP)

8. Intelligent DataBase System Lab, NCKU, Taiwan 8 Online: Recommender Offline :UPOI-Mine LBSN Dataset Location Types Social Links Check-in Data Individual Preference (IP) Social Factor (SF) POI popularity (PP) Feature Extraction

9. Intelligent DataBase System Lab, NCKU, Taiwan Social Factor (SF) 9 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* Weighted summation: Weight

10. Intelligent DataBase System Lab, NCKU, Taiwan Social Factor – Relation 10 Check-in Similarity (CheckSim) based on their check-in log Relative Distance Similarity (DisSim) based on their geographic distance

11. Intelligent DataBase System Lab, NCKU, Taiwan Relation – CheckSim 11 POI IDABCDE user i10250 user j010010 user k11000 user l11111 ……………… ijk… i010… j101… k010.. …………… Friend Indicator

12. Intelligent DataBase System Lab, NCKU, Taiwan Relation – DisSim 12 Distance  dissimilarity Max i =1000 ijk… i010010… j100050… k10500.. …………… ijk… i010… j101… k010 …………… Friend IndicatorDistance

13. Intelligent DataBase System Lab, NCKU, Taiwan Social Factor – Example 13 Relation: CheckSim(A, B) = 0.5 DisSim(A, B) = 0.03 User B #Check-ins at POI K : 10 #Total Check-ins : 100 User A Interest(B, POI K ) = POI k ?

14. Intelligent DataBase System Lab, NCKU, Taiwan Individual Preference (IP) 14 Individual Preference(IP) HPref i,h CPref i,c highlight category

15. Intelligent DataBase System Lab, NCKU, Taiwan Individual Preference – HPref & CPref 15 POIA(c1)B(c2)C(c2)D(c3)Total Highlighth1,h2 h2h3 Check-in count512210 HighlightHPref i,h H10.375 H20.5 H30.125 h1h2h3h4h5Total User1A,BA,B,CD Total5+15+1+220016 Userc1c2c3c4 User1AB,CD CategoryCPref i,c C10.5 C20.3 C30.2 proportion of check-ins of the label

16. Intelligent DataBase System Lab, NCKU, Taiwan Individual Preference – Example 16 There is only one category for one POI. There are many highlights for one POI. CategoryCPref Seafood0.5 Hotdog & Sausages 0.1 Fast food0.1 Steak0.3 User A’s pref table HighlightHPref Coffee0.5 Sightseeing0.1 Ice Cream0.1 Cheese0.3 POI Category: Hotdog & Sausages Highlight: Coffee(12), Cheese(88) POI Category: Hotdog & Sausages Highlight: Coffee(12), Cheese(88) Counts of highlight

17. Intelligent DataBase System Lab, NCKU, Taiwan Individual Preference – Example (cont.) 17 POI A Category: Hotdog & Sausages Highlight: Coffee(12), Cheese(88) POI A Category: Hotdog & Sausages Highlight: Coffee(12), Cheese(88) CPref HPref CategoryCPref Seafood0.5 Hotdog & Sausages 0.1 Fast food0.1 Steak0.3 User A’s pref table HighlightHPref Coffee0.5 Sightseeing0.1 Ice Cream0.1 Cheese0.3

18. Intelligent DataBase System Lab, NCKU, Taiwan POI Popularity (PP) 18 POI Popularity Relative Popularity of POI Normalized based on category

19. Intelligent DataBase System Lab, NCKU, Taiwan POI Popularity – Example 19 Frank Category: Hot Dogs Frank Category: Hot Dogs Hot Dogs Check-in count Frank4,032 KKK25 ……… total100,000

20. Intelligent DataBase System Lab, NCKU, Taiwan Online: Recommender Offline :UPOI-Mine LBSN Dataset Location Types Social Links Check-in Data Individual Preference (IP) Social Factor (SF) POI popularity (PP) Relevance Estimation 20

21. Intelligent DataBase System Lab, NCKU, Taiwan Relevance Estimation – Example 21 User IDPOI IDSFPPIPRelevance 1A0.20.10.0013 1B0.050.20.15 1C0.0040.10.91 ……………… ND0.50.150.062 Target Regression-Tree Model To estimate the relevance of each pair of user to POI, we use these feature to learn a Regression-Tree Model.

22. Intelligent DataBase System Lab, NCKU, Taiwan Relevance Estimation – Regression-Tree Model 22 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

23. Intelligent DataBase System Lab, NCKU, Taiwan 23 Online: Recommender Offline :UPOI-Mine LBSN Dataset Location Types Social Links Check-in Data Individual Preference (IP) Social Factor (SF) POI popularity (PP) Recommender

24. Intelligent DataBase System Lab, NCKU, Taiwan Experimental Evaluation 24 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

25. Intelligent DataBase System Lab, NCKU, Taiwan Experimental Evaluation 25 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

26. Intelligent DataBase System Lab, NCKU, Taiwan Experimental Evaluation (cont.) 26 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. POI ID Check-inRelevance A501 B 1 C5005 D2003 avg = 200

27. Intelligent DataBase System Lab, NCKU, Taiwan Comparison of Various Features 27 The Individual Preference is more important than Social Factor for urban POI recommendation.

28. Intelligent DataBase System Lab, NCKU, Taiwan Comparison of Various Features (cont.) 28

29. Intelligent DataBase System Lab, NCKU, Taiwan Comparison with Existing Recommenders 29

30. Intelligent DataBase System Lab, NCKU, Taiwan Comparison with Existing Recommenders (cont.) 30

31. Intelligent DataBase System Lab, NCKU, Taiwan Conclusions We proposed a novel urban POIs recommendation 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. Intelligent DataBase System Lab, NCKU, Taiwan Question? Thank you for your attentions