![Location Based Social Network
(LBSN)
2
[1] Bao, J., Zheng, Y., & Mokbel, M. F. (2012, November). Location-based and preference-aware recommendation using
sparse geo-social networking data. InProceedings of the 20th International Conference on Advances in Geographic
Information Systems (pp. 199-208). ACM.
[2] http://research.microsoft.com/en-us/projects/lbsn/](https://image.slidesharecdn.com/recsysmaps-170601215933/85/MAPS-A-Multi-Aspect-Personalized-POI-Recommender-System-2-320.jpg)
![Introduction
• Objective:
– Efficient location recommendation
• Challenges
– LBSN rating matrix => {user-check-ins} often sparse
– Difficult to have explicit user profile (age, preferences etc.)
– 96% of people share < 10% of commonly visited places and
87% of people share nothing at all [1]
– Cold start problem
• Solution:
– Fusion of major aspects (check-in time, location category,
social relation, location distance etc.) associated with LBSN
3
[1] M. Ye, P. Yin, and W.-C. Lee, “Location recommendation for location-based social networks,” in Proceedings of the 18th
SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2010, pp. 458–461.](https://image.slidesharecdn.com/recsysmaps-170601215933/85/MAPS-A-Multi-Aspect-Personalized-POI-Recommender-System-3-320.jpg)
![Methodology
• Location as a node of a graph and the bag of
{user, check-in time} tuple as its attributes
• Personalized Page Rank[6]
– categorical and spatial constraints
• Time constraint => check-ins within a time
interval
• Social constraint=> check-ins made by friends
• Location rank computed with constraints
6](https://image.slidesharecdn.com/recsysmaps-170601215933/85/MAPS-A-Multi-Aspect-Personalized-POI-Recommender-System-6-320.jpg)
![Dataset[1] statistics
Attributes Gowalla Weeplaces
Check-ins 36,001,959 7,658,368
Users 319,063 15,799
Locations 2,844,076 971,309
Social links (un-directional) 337,545 59,970
Location Categories 629 96
11
[1] X. Liu, Y. Liu, K. Aberer, and C. Miao, “Personalized point-of-interest recommendation by mining users’ preference
transition,” in Proceedings of the 22nd ACM international conference on Conference on information & knowledge
management. ACM, 2013, pp. 733–738.](https://image.slidesharecdn.com/recsysmaps-170601215933/85/MAPS-A-Multi-Aspect-Personalized-POI-Recommender-System-11-320.jpg)
![Results (Gowalla Dataset)
Models Precision Recall F-Score
Ye et al. [1] 0.03000 0.00120 0.00230
LBSNRank [2] 0.40900 0.00300 0.00600
Wang et al. [3] 0.10600 0.00200 0.00392
CLM 0.00633 0.00154 0.00247
SLM 0.25350 0.00973 0.01874
MAPS 0.35400 0.03100 0.05700*
16](https://image.slidesharecdn.com/recsysmaps-170601215933/85/MAPS-A-Multi-Aspect-Personalized-POI-Recommender-System-14-320.jpg)
MAPS: A Multi Aspect Personalized POI Recommender System
- 1. MAPS: A Multi Aspect Personalized POI Recommender System Ramesh Baral, Tao Li Florida International University Miami, FL September 18, 2016
- 2. Location Based Social Network (LBSN) 2 [1] Bao, J., Zheng, Y., & Mokbel, M. F. (2012, November). Location-based and preference-aware recommendation using sparse geo-social networking data. InProceedings of the 20th International Conference on Advances in Geographic Information Systems (pp. 199-208). ACM. [2] http://research.microsoft.com/en-us/projects/lbsn/
- 3. Introduction • Objective: – Efficient location recommendation • Challenges – LBSN rating matrix => {user-check-ins} often sparse – Difficult to have explicit user profile (age, preferences etc.) – 96% of people share < 10% of commonly visited places and 87% of people share nothing at all [1] – Cold start problem • Solution: – Fusion of major aspects (check-in time, location category, social relation, location distance etc.) associated with LBSN 3 [1] M. Ye, P. Yin, and W.-C. Lee, “Location recommendation for location-based social networks,” in Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2010, pp. 458–461.
- 4. Motivation • Geographical – Preference to near location • Temporal – Temporal popularity of POI – Temporal check-in trend of user • Categorical – Locations with same category can be an option • Social – Influence of Friends/Followers-Followee 4
- 5. Example 5
- 6. Methodology • Location as a node of a graph and the bag of {user, check-in time} tuple as its attributes • Personalized Page Rank[6] – categorical and spatial constraints • Time constraint => check-ins within a time interval • Social constraint=> check-ins made by friends • Location rank computed with constraints 6
- 7. Example 7
- 11. Dataset[1] statistics Attributes Gowalla Weeplaces Check-ins 36,001,959 7,658,368 Users 319,063 15,799 Locations 2,844,076 971,309 Social links (un-directional) 337,545 59,970 Location Categories 629 96 11 [1] X. Liu, Y. Liu, K. Aberer, and C. Miao, “Personalized point-of-interest recommendation by mining users’ preference transition,” in Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. ACM, 2013, pp. 733–738.
- 12. Check-in trend with distance 13
- 13. Evaluation parameters • 5-fold cross-validation • Cases: (i) top 5, (ii) top 10, (iii) top 15 items with the highest recommendation score • α =0.85 • Categorical model =0.75 and =0.25 • Spatial model = 0.75 and =0.25 • For unified model, categorical aspect = 0.25 14
- 14. Results (Gowalla Dataset) Models Precision Recall F-Score Ye et al. [1] 0.03000 0.00120 0.00230 LBSNRank [2] 0.40900 0.00300 0.00600 Wang et al. [3] 0.10600 0.00200 0.00392 CLM 0.00633 0.00154 0.00247 SLM 0.25350 0.00973 0.01874 MAPS 0.35400 0.03100 0.05700* 16
- 15. Conclusion and Future direction • Fused the (a) the geographical/spatial, (b) the categorical, (c) the temporal and (d) the social aspects into a POI recommendation model • Future direction: – Fusion of other aspects – Other domains 18
- 16. Acknowledgements • Anonymous reviewers • RecSys committee • National Science Foundation (NSF) grants CNS- 1126619, IIS-1213026, and CNS-1461926 • U.S. Department of Homeland Securitys VACCINE Center under Award Number 2009-ST-061- CI0001 • A gift award from Huawei Technologies Co.Ltd. 19
- 17. References 1. M. Ye, P. Yin, and W.-C. Lee, “Location recommendation for location-based social networks,” in Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2010, pp. 458–461. 2. Z. Jin, D. Shi, Q. Wu, H. Yan, and H. Fan, “Lbsnrank: personalized pagerank on location-based social networks,” in Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 2012, pp. 980–987. 3. H. Wang, M. Terrovitis, and N. Mamoulis, “Location recommendation in location- based social networks using user check-in data,” in Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2013, pp. 374–383. 4. X. Liu, Y. Liu, K. Aberer, and C. Miao, “Personalized point-of-interest recommendation by mining users’ preference transition,” in Proceedings of the 22nd ACM international conference on Conference on information & knowledge management. ACM, 2013, pp. 733–738. 5. Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann, “Time-aware point-of- interest recommendation,” in Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. ACM, 2013, pp. 363–372. 6. T. H. Haveliwala. Topic-sensitive pagerank. In Proceedings of the 11th international conference on World Wide Web, pages 517-526. ACM, 2002. 20
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