1. Geographical and Temporal Similarity Measurement in Location-based Social Networks Chongqing University of Posts and Telecommunications KTH – Royal Institute of Technology Zhengwu Yuan Yanli Jiang Gyözö Gidofalvi

2. Outline  Introduction  Related work  A hierarchical spatio-temporal similarity measure in LBSN  Empirical evaluations 2013-11-05MobiGIS 2013, Orlando, FL2

3. Introduction 2013-11-05MobiGIS 2013, Orlando, FL3 Mobile Internet technology Internet technology Space Location technology Location-based Social Network User Similarity

4. LBSN Applications 2013-11-05MobiGIS 2013, Orlando, FL4

5. Information Layout of LBSN Gao at al. Data Analysis on Location-Based Social Networks. 2011 2013-11-05MobiGIS 2013, Orlando, FL5

6. Outline  Introduction  Related work  A hierarchical spatio-temporal similarity measure in LBSN  Empirical evaluations 2013-11-05MobiGIS 2013, Orlando, FL6

7. Traditional: Cosine Similarity 2013-11-05MobiGIS 2013, Orlando, FL7  Given a set of commonly rated items I AB, the cosine similarity between two users A and B based on their respective ratings R A,i and R B,i on items i I AB is:

8. Traditional: Adjusted Cosine Similarity 2013-11-05MobiGIS 2013, Orlando, FL8  Given a set of commonly rated items I AB, the adjusted cosine similarity between two users A and B based on the sets of their individually rated items I A and I B and their average individual ratings on these items and is:

9. Traditional: Pearson Correlation Coefficient 2013-11-05MobiGIS 2013, Orlando, FL9  Given a set of commonly rated items I AB, the adjusted cosine similarity between two users A and B based on the sets of their individually rated items I A and I B and their average individual ratings on these items and is:

10. Similarity in LBSN  Similarity along (a combination of) different dimensions:  Content layer, e.g.: Ye’11, McKenzie’13  Social layer, e.g.: Ye’12  Geographical layer, e.g.: Li’08  Semantic locations / categories of locations, e.g.: Xiao’10, Bao’12, Ye’11  Temporal sequential similarity, e.g.: Li’08  Check-in temporal similarity, e.g.: Ye’11 2013-11-05MobiGIS 2013, Orlando, FL10

11. Outline  Introduction  Related work  A hierarchical spatio-temporal similarity measure in LBSN  Empirical evaluations 2013-11-05MobiGIS 2013, Orlando, FL11

12. A Hierarchical Spatial-Temporal Similarity Measure in LBSN  Assumptions about user similarity:  The closer is the time and the geographical location that two users access the more similar are the two users to each other  The larger is the number of check-ins of two users in nearby locations at similar times, the more similar are the two users to each other  Similarity changes with the level of detail  Proposed method:  Extract spatio-temporal clusters from user check-ins at different spatio-temporal levels of detail  For each ST level of detail, measure the cosine similarity between users using the classical Vector Space Model (VSM) with vectors composed of the amount of user visits in different ST clusters  Calculate the weighted combination of similarities at different ST levels of detail 2013-11-05MobiGIS 2013, Orlando, FL12

13. Hierarchical Spatio-Temporal Clustering  Spatio-temporal variant of DBSCAN: ST-DBSCAN [Birant’07]  An object is a core object if within its spatial (Eps_space) and temporal (Eps_time) neighborhood the number of objects is at least MinPts.  Definitions for Directly Density-Reachable (DDR), Density-Reachable (DR), and Density-Connected are straight forward extensions.  Clusters at different levels of detail: 2013-11-05MobiGIS 2013, Orlando, FL13

14. Vector Space Model Define the user-location matrix within a certain period as where m is the total number of users, n is the number of ST clusters discovered by ST-DBSCAN(Eps_space, Eps_time, MinPts), V ij is the number of check-ins by user i in the ST cluster j, and l is the level of detail in the clustering hierarchy. 2013-11-05MobiGIS 2013, Orlando, FL14

15. User Similarity  User similarity at a given cluster hierarchy level is according to the cosine similarity of the location vectors of the users:  The overall similarity of users is calculated across the cluster hierarchy levels as follows: 2013-11-05MobiGIS 2013, Orlando, FL15

16. Outline  Introduction  Related work  A hierarchical spatio-temporal similarity measure in LBSN  Empirical evaluations 2013-11-05MobiGIS 2013, Orlando, FL16

17. Dataset  Check-in datasets from Gowalla from the Stanford Network Analysis project for the US cities: 2013-11-05MobiGIS 2013, Orlando, FL17

18. Evaluation Metrics  Precision and recall (“relative overlap”) of the visits of a user u r and its most similar user u to the Top-N ST clusters / POIs: 2013-11-05MobiGIS 2013, Orlando, FL18

19. Results 2013-11-05MobiGIS 2013, Orlando, FL19  ST generalization at different levels of detail improves performance  Combining similarity measures at different ST levels of detail improves precision and recall and outperforms the fine-grained method (see ST-DBSCAN)  Considering the amount (not only the existence) of check- in at different ST clusters improves performance (see Jaccard)

20. Conclusions  We have proposed a new method to calculate the user similarity on LBSN based on the spatial and temporal properties of the user check-in data.  The method can be applied to recommend location or friends in LBSN, because the key of a recommendation system is the similarity measurement of user or item. 2013-11-05MobiGIS 2013, Orlando, FL20

21. Thank you for your attention! Q/A? 2013-11-05MobiGIS 2013, Orlando, FL21