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Mining User Similarity Based on Location History Yu Zheng, Quannan Li, Xing Xie Microsoft Research Asia.

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Presentation on theme: "Mining User Similarity Based on Location History Yu Zheng, Quannan Li, Xing Xie Microsoft Research Asia."— Presentation transcript:

1 Mining User Similarity Based on Location History Yu Zheng, Quannan Li, Xing Xie Microsoft Research Asia

2 Outline Introduction Architecture – Modeling Location History – Measuring User Similarity Experimental Results Conclusion

3 Introduction (1) Goals – Inferring the similarity (correlations ) between users from their location histories – Enable friend recommendation  Personalized location recommendation Motivation – The increasing availability of user-generated trajectories Life logging, Travel experience sharing Sports activity analysis, Multimedia content management,… – People’s outdoor movements in the real world imply their interests Like sports: if frequently visit gyms and stadiums Like Travel: if usually access mountains and lakes – According to the first law of the geography Everything is related to everything else, but near things are more related than distant things. People with similar location histories might share similar interests and preferences. – Significance of user similarity in Web communities Generally, it help users find more relevant information from a large-scale dataset In GIS community: friend discovering and location recommendation

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6 Introduction (2) Difficulty & Challenges – How to model different users’ location history uniformly Various users’ location histories are inconsistent and incomparable What’s a shared location? By distance ?? X – How to measure the similarity between users By counting the number of shared locations ?? The Pearson correlation and the cosine correlation ?? They do not take into account two important properties of people’s outdoor movements. Contribution and insights – A step towards integrating social networking into GIS – A hierarchical-graph Uniformly modeling different users’ location histories on a various scales of geo-spaces – A similarity measure considering Sequence property of users’ movement behavior Hierarchy property of geographic spaces

7 Preliminary GPS logs P and GPS trajectory Stay points S={s 1, s 2,…, s n }. – Stands for a geo-region where a user has stayed for a while – E.g., if a user spent more 20 minutes within a distance of 200 meters – Carry a semantic meaning beyond a raw GPS point Location history: – represented by a sequence of stay points – with transition intervals

8 Architecture (1) Modeling Location History Measuring Similarity A similarity score Sij for each pair of users A Hierarchical Graph for each individual

9 Modeling Location History (1) 1. Stay point detection 2. Hierarchical clustering 3. Individual graph building Measuring Similarity A similarity score Sij for each pair of users A Hierarchical Graph for each individual Modeling Location History

10 3. Individual graph building Modeling Location History (2) 1. Stay point detection 2. Hierarchical clustering

11 Measuring User Similarity (1) 1. Sequence Extraction 2. Sequence Matching 3. Similarity Score Calculating Measuring Similarity A similarity score Sij for each pair of users A Hierarchical Graph for each individual Modeling Location History

12 Measuring Similarity (2) Similar sequence Extraction,,

13 Measuring Similarity (3) Sequence matching – We aim to find out the maximum-length similar sequence – A pair of similar sequence: two individuals share the property of visiting the same sequence of places with a similar time interval ACAC A  B  C √ Same visiting order: a i == b i Similar transition time: ABAB B  A X X

14 Measuring Similarity (4) Similarity Calculating – Two factors The length of the matched similar sequence The level of the matched similar sequence – Calculation,, 1. Calculating similarity score for each sequence (weighted by its length) 2. Adding up similarity score of each sequence found on a level 3. Weighted Summing up the score of multiple levels

15 Measuring Similarity (5) User 2: b  d User 1: A  B User 1: a  c  e User 1: A  B User 3: A  B A  B c  e A  B User 1: a  c  e User 2: A  B User 3: b  c  e User 1: User3> User 2

16 Experiments (1) GPS Devices and Users – 112 users collecting the data in the past year

17 Experiments (2) GPS dataset – > 6 million GPS points – > 170,000 kilometers – 36 cities in China and a few city in the USA, Korea and Japan

18 Experiments (3) Relevance levelRelationships suggestion 4Strongly similarFamily members/intimate lovers/roommate 3SimilarGood friends/workmates/classmates 2Weakly similarOrdinary friends, neighbors in a community 1DifferentStrangers in the same city 0Quite differentStrangers in other cities Evaluation approach – Evaluated as an information retrieval problem – Ground truth: Users label the relationship with a ratings show in this Table

19 Experiments (4) Comparing with baselines – The Pearson Correlation – Cosine Similarity

20 Experiments (5) NDCG comparison

21 Conclusion A hierarchical graph – A uniform framework to measure various users’ location histories – Effectively modeling users’ outdoor movements Sequentially Hierarchically Our similarity measurement outperformed existing methods – The Person measurement and – Cosine similarity measurement – Hierarchy + Sequence achieved the best performance

22 Thanks! Microsoft Research Asia


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