1 / 21

Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks

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. Location Recommendation on LBSNs. More choices of life experience than before.

regina-best
Download Presentation

Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Exploring Temporal Effects for Location Recommendationon 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 • Information Layout of LBSNs • Social Influence • Geographical Influence • Geo-social • Correlations Not Explored in Depth

  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 • 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 • 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.

  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 next time status Consecutiveness Similarity u1 random time status Non-Consecutiveness Similarity H0: P<=D; H1: P>D The null hypothesis is rejected at significant level α = 0.001 with p-value of 5.6135e-191

  7. Location Recommendation with NMF • Basic Location Recommendation without Temporal Effects

  8. Location Recommendation Model • Location Recommendation with Temporal Effects • Temporal Non-uniformness • A user presents different check-in preferences at different hour of the day. t24 t1 t2 t2 …… t24 t1 t2 t2 ……

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

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

  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 • Ensemble • Sum • Maximum

  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. 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 10-5density.

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

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

  20. Acknowledgments • Co-Authors • Office of Naval Research (ONR) • 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 effects 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

More Related