1. 15th CTI Workshop, July 26, 2008 1 Smart Itinerary Recommendation based on User-Generated GPS Trajectories Hyoseok Yoon 1, Y. Zheng 2, X. Xie 2 and W. Woo 1 1 GIST U-VR Lab. 2 Microsoft Research Asia

2. Traveling Popular leisure activityPopular leisure activity How to use time wisely? Trial-and-error is COSTLY!!! Photo

3. Commercial Solution Handful itinerariesHandful itineraries –Major location –Fixed time Not flexibleNot flexible Photo

4. Social Solution Ask residents of the region Refer to travel experts Learn from the experienced Photo

5. Introduction Data mining of GPS trajectories –User-generated –Travel routes –Travel experiences Itinerary recommendation

6. Related Work Itinerary Recommendation –Interactive system for manually generate itinerary INTRIGUE, TripTip –Travel recommendation system based on online travel info. (Huang and Bian) –Advanced Traveler Information System based on the shortest distance GPS Data Mining Applications –Finding patterns in GPS trajectory –Find locations of interest –GeoLife: mine user similarity, interest locations, and travel sequences

7. Contributions Build Location-Interest Graph –From multiple user-generated GPS trajectories –For modeling travel routes Define a good itinerary –How to define and model itinerary –How it can be evaluated Smart itinerary recommendation framework –Recommend highly efficient and balanced itinerary Evaluation –Using a large GPS dataset –Simulated/real user queries

8. Preliminaries Trajectory: a sequence of time-stamped points Stay Point: a geographical region s –Where a user stayed over a time threshold within a distance threshold

9. Preliminaries Location History: A sequence of stay points user visited Locations: Clusters of stay points detected from multiple users trajectories –Substitute a stay point in with the Location ID the stay point pertains to Location s s s s s s s s s s

10. Preliminaries Typical Stay Time: Defined as median of stay time of stay points in l i Typical Time Interval (T i,j ): Traveling time between location l i to l j Location s s s s s s s s s s s s s s s s s s s s s s

11. Preliminaries Location Interest –The interest of a location is represented by authority scores (HITS-based inference model)* –User Experience as Hub –Locations as Authority *Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining Correlation Between Locations Using Human Location History, In: GIS 2009, pp. 472-475 (2009)

12. Preliminaries Trip: A sequence of locations with corresponding typical time intervals Itinerary: A recommended trip based on user query Q User Query: A user-specified input (start point, end point and duration)

13. Modeling Itinerary Duration as the constraint –Duration that exceeds users requirement No use to users –Simplifies algorithmic complexity Provides a stopping condition

14. First three factors to find candidate trips –(1) Elapsed Time Ratio –(2) Stay Time Ratio –(3) Interest Density Ratio Classical travel sequence to differentiate candidates further –(4) Classical Travel Sequence Ratio Modeling Itinerary

15. Architecture Offline –Analyze collected GPS trajectories –Build a Location- Interest Graph (G r ) Online –Use G r to recommend an itinerary based on user query

16. Location-Interest Graph –(1) Detect stay points –(2) Cluster them into locations –(3) Calculate location interest –(4) Compute classical travel sequence* We build G r offline which contains info. on – Location itself interest, typical staying time –Relationship between locations Typical traveling time, classical travel sequence *Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining Interesting Locations and Travel Sequences from GPS Trajectories. In: WWW 2009, pp. 791-800 (2009)

17. Query Verification In the online process, user query Q needs to be verified by calculating Dist(q s,q d ) –(1) Using GPS coordinates Harversine formula or the spherical law of cosines –(2) Use Web service such as Bing Map If the query is reasonable –Substitute start point and the end point with the nearest locations in G r –Send an updated query Q` = {l s,l d,q t } to recommender

18. Trip Candidate Selection Select trip candidates from the starting location l s to the end location l d. Candidate trips do not exceed the given duration q t. –(1) start by adding l s to the trip –(2) Add next feasible location not in the trip –(3) Update time parameter –(4) Repeat until the end location is reached or no more location can be added

19. Trip Candidate Ranking Top-k trips in the order of the Euclidean Distance of ( Elapsed Time Ratio, Stay Time Ratio, Interest Density Ratio)

20. Re-ranking by Travel Sequence Differentiate candidates further with classical travel sequence to consider –Authority score of going in and out and the hub scores Re-rank with CTSR

21. Illustrative Example 1H 2H 1H 1.5H 1H 30M 40M

22. Experiments Settings –GPS trajectories collected from 125 users 17,745 GPS trajectories (May. 2007 ~ Aug. 2009 in Beijing) –Time threshold T r (20 min), distance threshold D r (200 meters) –35,319 stay points are detected excluding work/home spots –Density-based clustering algorithm OPTICS to result in 119 location

23. Experiments Two evaluation approach (1) Simulated user queries –Algorithmic level comparison –Compare quality with baselines (2) User study with local residents –How users perceived quality of itineraries compare by different methods

24. Experiments Simulation –Four different levels for duration (5, 10,15, 20 hours) –For each level, 1,000 queries are generated User Study –10 active residents of Beijing (avg: 3.8 years) –Submitted 3 queries and score 3 itineraries generated by our method and two baselines (3x3).

25. Evaluation (Baselines) Ranking-by-Time (RbT) –Recommend an itinerary with the highest elapsed time usage Ranking-by-Interest (RbI) –Ranks the candidates in the order of total interest of locations included in the itinerary

26. Results In 5hr level, –All three produce similar quality results –There are not many candidates and they would overlap anyway

27. Results In 10hr-20hr level –Baseline algorithms only perform well in one aspect –Our algorithm produces well- balanced and classical sequence is considered

28. Results In 10hr-20hr level –Baseline algorithms only perform well in one aspect –Our algorithm produces well- balanced and classical sequence is considered

29. Results In 10hr-20hr level –Baseline algorithms only perform well in one aspect –Our algorithm produces well- balanced and classical sequence is considered

30. Results In 5hr level, –All three produce similar quality results –There are not many candidates and they would overlap anyway In 10hr-20hr level –Baseline algorithms only perform well in one aspect –Our algorithm produces well-balanced and classical sequence is considered

31. Results How does our method compare to RbT in terms of perceived time use? How does our method compare to RbI in terms of perceived interest? No significant advantage from RbT in perceived time or RbI in perceived interest Our method is well balanced and competitive

32. Conclusion Based on user-generated GPS trajectories –Build Location-Interest Graph –Model and define good itinerary Recommend itinerary based on user query –Find candidates and rank considering three factors ( Elapsed time, stay time and interest density ) –Re-rank with classical travel sequence Evaluated with real and simulated user query Future Work –Personalized recommendation using user preference

33. Context-Aware Mobile Augmented Reality 15th CTI Workshop, July 26, 2008 GIST U-VR Lab, Gwangju 500-712, Korea E-Mail: hyoon@gist.ac.kr Web: http://wiki.uvr.gist.ac.kr/Main/HyoseokYoon Discussions and More information