1. Data Mining and Machine Learning Lab Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu Data Mining and Machine Learning Lab Arizona State University

2. Location Recommendation on LBSNs  More choices of life experience than before Location-Based Social Networking (LBSNs)  Recommendation is indispensable  Help users filter uninteresting items.  Reduce time in decision making.  Location Recommendation on LBSNs  Recommend new points of interest (POIs) to a user according to his personal preferences

3. Location Recommendation on LBSNs Not Explored in Depth  Social Influence  Geographical Influence Geo-social Correlations Information Layout of LBSNs

4. Motivation  What temporal patterns can be observed from an individual user’s mobile behavior on LBSNs. Discover individual temporal patterns on LBSNs  How to leverage the temporal patterns for location recommendation? Propose a location recommendation framework with individual temporal patterns modeled.  How strong are the temporal patterns for improving location recommendation performance? Evaluate proposed framework on real-world LBSN dataset

5. Discovering Temporal Patterns on LBSNs  Temporal Non-uniformness A user presents different check-in preferences at different hour of the day.  Temporal Consecutiveness A user presents similar check-in preferences at nearby hour of the day.  One user’s daily check-in activity w.r.t. his top 5 frequently visited locations Figure 1: One User’s Daily Check-in at Five Locations

6. Hypothesis Testing  Temporal Non-uniformness A user presents different check-in preferences at different hour of the day.  Temporal Consecutiveness A user presents similar check-in preferences at adjacent hours of the day u1u1 next time status random time status H0: P D The null hypothesis is rejected at significant level α = 0.001 with p-value of 5.6135e-191 Consecutiveness Similarity Non-Consecutiveness Similarity

7. Check-in indicatorUser-Location matrix Low-rank representation of user check-in preference Low-rank representation of location preference User i has checked-in at location j Location Recommendation with NMF  Basic Location Recommendation without Temporal Effects

8. Location Recommendation Model  Temporal Non-uniformness A user presents different check-in preferences at different hour of the day.  Location Recommendation with Temporal Effects t1t1 t2t2 t2t2 t 24 …… t1t1 t2t2 t2t2 t 24 ……

9. Location Recommendation Model  Temporal Consecutiveness A user presents similar check-in preferences at nearby hour of the day  Location Recommendation with Temporal Effects

10. Location Recommendation Model Updating Rules:  Location Recommendation with Temporal Effects Temporal Consecutiveness Temporal Non-uniformness

11. Location Recommendation Framework  LRT: Location Recommendation Framework with Temporal Effects Unobserved Check-ins Approximated Check-in Preference T=24

12. Location Recommendation Framework  Temporal Aggregation  Sum  Maximum  Ensemble

13. Experiments  Dataset: Foursquare  Training/Testing Data: For each individual, randomly mark off 20%, 40% of all locations that he has checked-in for testing, the rest are used as training.  Evaluation Metrics: Precision@N, Recall@N

14. TestingMetricsSumMaxEnsemble 20% R@51.60%1.57%1.71% R@103.05%3.03%3.11% 40% R@51.73%1.74%1.79% R@103.25%3.30%3.35% TestingMetricsSumMaxEnsemble 20% P@51.37%1.35%1.47% P@101.31%1.30%1.34% 40% P@53.08%3.10%3.20% P@102.95% 3.00% Experiments  Temporal Aggregation  Ensemble  Sum  Maximum Precision Recall

15. Experiments  Recommendation effectiveness w.r.t. to the data sparseness  The effectiveness of recommender systems with sparse dataset (i.e., low-density user-item matrix) is usually not high.  The reported P@5 is 5% over a data with 8.02 x 10 -3 density, and 3.5% over a data with 4.24 x 10 -5 density.

16. Experiments  Performance Comparison  Memory-Based Collaborative Filtering (CF)  Non-Negative Matrix Factorization (NMF)  LRT (Ensemble)  Test=20% P@5, R@5  Test=20% P@10, R@10  Test=40% P@5, R@5  Test=40% P@10, R@10

17. Experiments  Performance Comparison  Random LRT (R-LRT)  LRT (Ensemble)  Test=20% P@5, R@5  Test=20% P@10, R@10  Test=40% P@5, R@5  Test=40% P@10, R@10

18. Extension of LRT to Various Temporal Patterns  Apply LRT with Different Temporal Patterns

19. Extension of LRT to Various Temporal Patterns  Comparison of Temporal Patterns  Day of the Week  Weekday/Weekend Temporal PatternsMetrics@5@10 Day of the Week Precision2.32%2.18% Recall1.30%2.45% Weekday/Weekend Precision2.23%2.04% Recall1.21%2.28%

20.  Co-Authors  Office of Naval Research (ONR) Acknowledgments  Data Mining and Machine Learning Lab (DMML) @ ASU http://dmml.asu.edu/

21. Conclusions and Future Work  Investigated individual temporal patterns of user check-in behavior on LBSNs  Propose a location recommendation framework with temporal e ff ects and evaluate it on a real-world dataset  Future Work  Explore other temporal patterns (e.g., monthly/ yearly patterns)  Study the complementary effects of different kind of temporal patterns