Mining Interesting Locations and Travel Sequences from GPS Trajectories IDB & IDS Lab. Seminar Summer 2009 강 민 석강 민 석 July 23 rd, 2009 Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma WWW 2009 Center for E-Business Technology Seoul National University Seoul, Korea Microsoft Research Asia Intelligent Database Systems Lab.

Copyright  2009 by CEBT Abstract  Mining Interesting Locations and Travel Sequences from GPS Trajectories GPS log : record users’ outdoor movements with GPS By mining multiple users’ location histories, discover interesting locations and travel sequences in a given region  Problem How to model multiple users’ location history from GPS log How to infer the interest level of a location Location interest not only depend on the number of visiting, but also users’ travel experiences. How to detect classical sequences in a given region 2 timestampLatitudelongitude :30:00N 33º 30’ 19.5”E 126º 29’ 35.3” :30:30N 33º 30’ 19.4”E 126º 29’ 35.2” :31:00N 33º 30’ 19.2”E 126º 29’ 35.3” :31:30N 33º 30’ 19.1”E 126º 29’ 35.3” :32:00N 33º 30’ 19.1”E 126º 29’ 35.4” timestampLatitudelongitude :30:00N 33º 30’ 19.5”E 126º 29’ 35.3” :30:30N 33º 30’ 19.4”E 126º 29’ 35.2” :31:00N 33º 30’ 19.2”E 126º 29’ 35.3” :31:30N 33º 30’ 19.1”E 126º 29’ 35.3” :32:00N 33º 30’ 19.1”E 126º 29’ 35.4” timestampLatitudelongitude :30:00N 33º 30’ 19.5”E 126º 29’ 35.3” :30:30N 33º 30’ 19.4”E 126º 29’ 35.2” :31:00N 33º 30’ 19.2”E 126º 29’ 35.3” :31:30N 33º 30’ 19.1”E 126º 29’ 35.3” :32:00N 33º 30’ 19.1”E 126º 29’ 35.4”

Contents  Introduction  Modeling Location History  Location Interest Inference  Experiments  Related Work  Conclusions 3

Copyright  2009 by CEBT Introduction  GPS log Recently, many users record their outdoor movements with GPS. Travel experience sharing, Life Logging, Sports activity GPS devices are changing the way people interact with the Web by using locations as contexts. 4

Copyright  2009 by CEBT Introduction  GPS log Let’s look at my GPS Trajectories! 5

removed some photos for privacy 6

Copyright  2009 by CEBT 7

Introduction  Architecture System comprises of three parts Location history modeling, location interest & sequence mining, recommendation 8 Tree-Based Hierarchical Graph HITS-Based Inference Model User Travel Experience Location Interest Location History Modeling Location Interest and Sequence Mining Recommendation Modeling Location History GPS Logs Experienced Users Interesting Locations Travel Sequences Mining Travel Sequences Location Recommender

Contents  Introduction  Modeling Location History GPS Trajectory & Stay Point Location History Tree-Based Hierarchical Graph (TBHG)  Location Interest Inference  Experiments  Related Work  Conclusions 9

Copyright  2009 by CEBT Modeling Location History  GPS Trajectory GPS point : contain (timestamp, latitude, longitude) GPS log : a collection of GPS points GPS trajectory : sequentially connect GPS points  Stay Point geographic region where a user stayed over a certain period time interval Time threshold T : stay over T (e.g. 20 min) Distance threshold D : distance between two points is less than D (e.g. 200 m) 10 timestampLatitudelongitude :30:00N 33º 30’ 19.5”E 126º 29’ 35.3” :30:30N 33º 30’ 19.4”E 126º 29’ 35.2” :31:00N 33º 30’ 19.2”E 126º 29’ 35.3” :31:30N 33º 30’ 19.1”E 126º 29’ 35.3” :32:00N 33º 30’ 19.1”E 126º 29’ 35.4” :32:30N 33º 30’ 19.1”E 126º 29’ 35.4” :33:00N 33º 30’ 19.2”E 126º 29’ 35.4”

Copyright  2009 by CEBT Modeling Location History  Location History represented as a sequence of stay points with corresponding arrival and leaving times 11 S1 S2 S3 S4 S5 S6 S7 Home Supermarket Company Restaurant S8 S9 S1 0

Copyright  2009 by CEBT Modeling Location History  Model multiple users’ location histories Location history of various people are inconsistent and incomparable stay points of different individuals are not identical  Considering the scale of location 12 A B S1 S2 S3 S4 S5 S6 S7 Home Supermarket Company Restaurant S8 S9 S1 0 C1 C2 C3 C4

Copyright  2009 by CEBT Modeling Location History  Tree-Based Hierarchy Build a tree using a hierarchical clustering algorithm Density-based clustering algorithm OPTICS (Ordering Points to Identify the Clustering Structure) Hierarchically cluster stay points into some geospatial regions Different levels denote different geospatial granularity 13

Copyright  2009 by CEBT Modeling Location History  Tree-Based Hierarchical Graph (TBHG) 1.Formulate a Tree-based Hierarchy Hierarchically cluster stay points 2.Build Graphs on each Level Link is generated when consecutive stay points are contained in two clusters 14

Copyright  2009 by CEBT Modeling Location History  Tree-Based Hierarchical Graph (TBHG) location history can be represented by a sequence of stay point clusters with transition time between two clusters on different geospatial scales 15 S1 S2 S3 S4 S5 S6 S7 Home Supermarket Company Restaurant S8 S9 S1 0 C1 C2 C3 C4 S1 S2 S3 S4 S5 S6 S7 S8 S9 S1 0 A B

Contents  Introduction  Modeling Location History  Location Interest Inference HITS-Based Inference Model Mining Classical Travel Sequences  Experiments  Related Work  Conclusions 16

Copyright  2009 by CEBT Location Interest Inference  HITS (Hypertext Induced Topic Search) search query dependent ranking algorithm for Web IR produce two rankings Hub : web page with many out-links Authority : web page with many in-links Hub and Authority have a mutual reinforcement relationship 17

Copyright  2009 by CEBT Location Interest Inference  HITS-Based Inference Model regard an user’s visit to a location as an implicitly directed link from the user to that location Hub and Authority Hub : a user who has accessed many places → users’ travel experiences Authority : a location which has been visited by many users → location interest mutual reinforcement relationship Users’ travel experiences (hub scores) & interest of locations (authority scores) 18

Copyright  2009 by CEBT Location Interest Inference  Data Selection Strategy Motivation User’s travel experience is region-related. need to specify a geospatial region before conducting HITS-based inference Strategy calculate scores using regions specified by their ascendant clusters can have multiple authority and hub scores based on the different region scales 19

Copyright  2009 by CEBT Location Interest Inference  Inference Build adjacent matrix between users and locations mutual reinforcement relationship of user travel experience and location interest Iterative process for generating the final results Calculate authority and hub scores using the power iteration method 20

Copyright  2009 by CEBT Mining Classical Travel Sequences  calculate Score for each Location Sequence the Travel Experiences of Users taking this sequence Hub scores of the user the Interests of the Locations contained in the sequence Authority scores of the locations in this sequence 21 5 users have taken A→C We know each user’s hub score. What is the classical score of sequence A→C→D TBHG We know location C’s authority score.

Copyright  2009 by CEBT Mining Classical Travel Sequences  calculate Score for each Location Sequence the Travel Experiences of Users taking this sequence Hub scores of the user the Interests of the Locations contained in the sequence Authority scores of the locations in this sequence Authority scores are weighted based on the probability to take sequence 22 What is the classical score of sequence A→C→D Authority score of location A Hub score of Users Probability of moving out from A to this sequence

Contents  Introduction  Modeling Location History  Location Interest Inference  Experiments  Related Work  Conclusions 23

Copyright  2009 by CEBT Experimental Settings  GPS Data GPS devices to collect data Users 107 users record their outdoor movements get payments based on the distance of GPS log Data mostly in China, some in the USA, Korea, Japan 1 year (from May 2007 to Oct. 2008) 5 million GPS points (166,372 km)  Parameter Stay Point extracted 10,354 stay points Clustering 159 clusters (4 th level TBHG) 24

Copyright  2009 by CEBT Evaluation Approaches  Evaluation Explore effectiveness of location & travel recommendation by a user study 29 subjects who have been in Beijing for more that 6 years  Two Aspects of Evaluation Presentation the ability of the retrieved interesting locations in presenting a given region Representative, Comprehensive, Novelty Rank The ranking performance of the retrieved locations based on relative interests User Desirability Rating on each location & each sequence employ two criteria – nDCG and MAP  Baseline Interesting Locations rank-by-count, rank-by-frequency Classical Travel Sequences rank-by-count, rank-by-interests, rank-by-experience 25

Copyright  2009 by CEBT Experimental Results  Results outperformed baseline approaches  Investigations Advantages of the hierarchy of the TBHG Help users understand the region step-by-step (level-by-level) can be used to specify users’ travel experiences in different regions 26

Contents  Introduction  Modeling Location History  Location Interest Inference  Experiments  Related Work Mining Location History Location Recommenders  Conclusions 27

Copyright  2009 by CEBT Related Work  Mining Location History Individual location history Detect significant locations of a user Predict user’s movement Recognize user-specific activities at each location Multiple users’ location history Mining similar sequences Predict where a driver may be going Recognize the social pattern in daily user activity 28

Copyright  2009 by CEBT Related Work  Location Recommenders Recommenders based on real-time location Mobile Tourist Guide System Recommenders based on location history More Personalized recommendation using location history Recommend geographic locations like shops or restaurants Enhance collaborative filtering solution 29

Contents  Introduction  Modeling Location History  Location Interest Inference  Experiments  Related Work  Conclusions 30

Copyright  2009 by CEBT Conclusion  Mining Interesting Locations and Travel Sequences from GPS propose a tree-based hierarchical graph (TBHG), which can model multiple users’ location history propose a HITS-based model to infer users’ travel experiences and interest of a location within a region consider users’ travel experiences and location interests, and mine travel sequences evaluate methodology using large GPS dataset 31 Tree-Based Hierarchical Graph HITS-Based Inference Model User Travel Experience Location Interest Location History ModelingLocation Interest and Sequence Mining Recommendation Modeling Location History GPS Logs Experienced Users Interesting Locations Travel Sequences Mining Travel Sequences Location Recommender

Copyright  2009 by CEBT Conclusion  Implications Help understand the correlation between users and locations Enable location and travel recommendation Step towards enhancing mobile Web from multiple users’ location histories Improve location-based services by integrating social networking into mobile Web  GeoLife project Building social networks using human location history a location-based social-networking service on Microsoft Virtual Earth. enables users to share life experiences and build connections among each other using human location history. 32

Copyright  2009 by CEBT Discussion  Discussion about this paper (talked with Sungchan) Modeling Location History Stay point detection is simple and easy to apply Hierarchy model is appropriate to zoom in/out map HITS-based Location Interest Inference Pretty Reasonable : consider user’s travel experience is better than rank-by-count But, try another way to find location interest and user travel experience Travel Sequence too naïve for calculating sequence score  Motivation Context-aware Service Time + Location 33

Copyright  2009 by CEBT References  This Slide Some Images from GeoLife : Building social networks using human location history, Microsoft Research Y. Zheng, Mining Individual Life Pattern Based on Location History: A Paradigm and Framework, Slide, 2009 References [5], [7], [14], [18]  GeoLife Project Paper Yu Zheng and Xing Xie, Mining Individual Life Pattern Based on Location History, IEEE, 2009 Yu Zheng, Xing Xie, and Wei-Ying Ma, GeoLife2.0: A Location-Based Social Networking Service, IEEE, 2009 Yu Zheng, Xing Xie, and Wei-Ying Ma, Mining Interesting Locations and Travel Sequences From GPS Trajectories, ACM, 2009 Quannan Li, Yu Zheng, Xing Xie, and Wei-Ying Ma, Mining user similarity based on location history, ACM, 2008 Yu Zheng, Xing Xie, and Wei-Ying Ma, Understanding mobility based on GPS data, ACM, 2008 Yu Zheng and Xing Xie, Learning Transportation Mode from Raw GPS Data for Geographic Application on the Web, ACM,

35 Clustering the Tagged Web  Thank you~