Mining User Similarity from Semantic Trajectories

1. Mining User Similarity from Semantic Trajectories Josh Jia-Ching Ying, Eric Hsueh-Chan Lu, Wang-Chien Lee, Tz-Chiao Weng, and Vincent S. Tseng Presenter: Josh Jia-Ching Ying

2. Outline • Introduction • Semantic Trajectory Based Friend Recommendation • Experiment • Conclusion and future work

3. Introduction • With the rapid growth and fierce competition in the market of social networking services, many service providers have deployed various recommendation services • friend recommendation • Most of the friend recommendation engines use profiles or on-line behavior of users to make recommendations instead of capturing the ‘’real’’ characteristics in user behavior • In recent years, a new breed of social networking services, called location-based social networks (LBSNs), have emerged

4. Introduction • Obviously user similarity plays a crucial role in these friend recommendation services • Most studies of measuring mobile users’ similarity focus only on analyzing geographicfeatures of user trajectories • The notion of semantic trajectoryhas been proposed by Alvares et al. in 2007 • sequence of locations with semantic tags to capture the landmarks passed by • eg. School  Park  Restaurant

5. Introduction

6. Introduction

7. 1 2 3 Semantic Trajectory Semantic Trajectory Pattern Sets User Similarity Measurement Semantic Trajectory Transformation Semantic Trajectory Pattern Mining User Similarity Matrix 4 Potential Friends Recommender Geographic Information Trajectory logs Smart-phones or PDAs Laptops or PCs input Semantic Trajectory Based Friend Recommendation • Framework

8. Semantic Trajectory Transformation • GPS trajectory • Cell trajectory (Eagle et al. ) - 8 -

9. Semantic Trajectory Transformation • For GPS trajectory (basically follow Alvares et al’s approach) • US Post Office  Seniore’s Pizza  Fremont Park • Post Office  Restaurant  Park

10. Semantic Trajectory Transformation • For Cell trajectory • If the stay time > time threshold, the cell is calleda stay cell. stay time = leave time – arrive time

11. Semantic Trajectory Transformation • <Stay Cell0, Stay Cell1, Stay Cell2, Stay Cell3> • <{Unknown}, {School, Park }, {Park}, {Hospital}>

12. 1 2 3 Semantic Trajectory Semantic Trajectory Pattern Sets User Similarity Measurement Semantic Trajectory Transformation Semantic Trajectory Pattern Mining User Similarity Matrix 4 Potential Friends Recommender Geographic Information Trajectory logs Smart-phones or PDAs Laptops or PCs input Framework

13. User Similarity Measurement • Maximal Semantic Trajectory Pattern Similarity (MSTP-Similarity) • Similarity between two Semantic Trajectory Pattern Sets <A,{BC}> … <A,{BC},E> … <D,{AC},E> … <D,{AC},E> … - 13 -

14. MSTP-Similarity • Common part • Given two Maximal Semantic Trajectory Patterns, we argue that they are more similar when they have more common parts • the longest common sequence (LCS) of the two patterns - 14 -

15. MSTP-Similarity • The participation ratio of the common part to a pattern - 15 -

16. MSTP-Similarity • Pattern similarity • Equal Average • Weighted Average - 16 -

17. Similarity between two Users • To measure how similar two pattern sets are: • Equal weight • Weighting by support • Weighting by TFIDF user V user U P1 … Pm P1’ … Pn’ There are m×n Maximal Semantic Trajectory Pattern Similarity - 17 -

18. Weighting by support • A pattern with a high support is more important • Geometric mean • Arithmetic average user V user U P1 … Pm P1’ … Pn’ - 18 -

19. TFIDF <A,{BC}>: 6 <A,D> : 2 User a • TFIDF=TF*log(IDF) • TF: term frequency • IDF: inverse document frequency • Term frequency • User a • <A,{BC}>: 6/(6+2) = 3/4 • <A,D> :2/(6+2) = 1/4 • User b • <A,{BC}>: 3/(3+6) = 1/3 • <B,E> :6/(3+6) = 2/3 • User c • <A,{BC}>: 3/(3+2) = 3/5 • <A,D> :2/(3+2) = 2/5 • Inverse document frequency • <A,{BC}>: 3/(1+1+1) = 1 • <A,D> : 3/(1+0+1) = 3/2 • <B,E> : 3/(0+1+0) = 3 <A,{BC}>: 3 <B,E> : 6 User b <A,{BC}>: 3 <A,D> : 2 User c • TFIDF in User a: • <A,{BC}>: (3/4)*log1 = 0 • <A,D>: (1/4)*log1.5 = 0.04 - 19 -

20. Using TFIDF as the weight • Geometric mean • Arithmetic average user V user U P1 … Pm P1’ … Pn’ - 20 -

21. Experiment — dataset • MIT reality mining dataset • The Reality Mining project was conducted from 2004-2005 at the MIT Media Laboratory • Cell trajectory • Cell annotation

22. Experiment — ground truth • Ground truth • MIT Media Laboratory has conducted an online survey, which was completed by 94 mobile users • The survey data present the summarized behavior of a mobile user • Among the 94 mobile users, • 7 users who do not have cell trajectory logs, • 10 users who do not have cell annotation logs. • remaining 77 mobile users are used in our experiments

23. Experiment • Baseline • We directly perform a maximal sequential pattern mining algorithm on the stay cell sequence set for each mobile user

24. Conclusion and future work • We propose a novel framework to support friend recommendation services • the semantic trajectories of mobile users • MSTP-Similarity • for measuring the similarity between two semantic trajectory patterns • Through a series of experiments, our proposed friend recommendation framework has excellent performance under various conditions • Future work • Consider stay time (Duration) for the recommender • Consider geographic features for the recommender

25. Thank you for your attention Question?