Data Mining and Machine Learning Lab

Exploring Temporal Effects for Location Recommendationon Location-Based Social Networks

Huiji Gao, Jiliang Tang, Xia Hu, and Huan Liu

Data Mining and Machine Learning LabArizona State University

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

Location Recommendation on LBSNs

Not Explored

in Depth

Social Influence

Geographical Influence

• Geo-social Correlations

Information Layout of LBSNs

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

Discovering Temporal Patterns on LBSNs

Temporal Non-uniformnessA user presents different check-in preferences at different hour of the day.

Temporal ConsecutivenessA 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

Hypothesis Testing

Temporal Non-uniformnessA user presents different check-in preferences at different hour of the day.

Temporal ConsecutivenessA user presents similar check-in preferences at adjacent hours of the day

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H0: P<=D; H1: P>DThe null hypothesis is rejected at significant level α = 0.001 with p-value of 5.6135e-191

Consecutiveness Similarity

Non-Consecutiveness Similarity

Check-in indicator User-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

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Location Recommendation with Temporal Effects

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Temporal ConsecutivenessA user presents similar check-in preferences at nearby hour of the day

Location Recommendation with Temporal Effects

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Location Recommendation with Temporal Effects

TemporalConsecutiveness

Temporal Non-uniformness

Location Recommendation Framework

LRT: Location Recommendation Framework with Temporal Effects

Unobserved Check-ins Approximated

Check-in Preference

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Location Recommendation Framework

Temporal Aggregation

Sum

Maximum

Ensemble

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

Testing Metrics Sum Max Ensemble

20%R@5 1.60% 1.57% 1.71%

R@10 3.05% 3.03% 3.11%

40%R@5 1.73% 1.74% 1.79%

R@10 3.25% 3.30% 3.35%

Testing Metrics Sum Max Ensemble

20%P@5 1.37% 1.35% 1.47%

P@10 1.31% 1.30% 1.34%

40%P@5 3.08% 3.10% 3.20%

P@10 2.95% 2.95% 3.00%

Experiments

Temporal Aggregation

Ensemble Sum Maximum Precision

Recall

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.

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

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

Extension of LRT to Various Temporal Patterns

Apply LRT with Different Temporal Patterns

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Extension of LRT to Various Temporal Patterns

Comparison of Temporal Patterns

Day of the Week

Weekday/Weekend

Temporal Patterns Metrics @5 @10

Day of the WeekPrecision 2.32% 2.18%

Recall 1.30% 2.45%

Weekday/WeekendPrecision 2.23% 2.04%

Recall 1.21% 2.28%

Co-Authors

Office of Naval Research (ONR)

Acknowledgments

Data Mining and Machine Learning Lab (DMML) @ ASU

http://dmml.asu.edu/

Conclusions and Future Work

Investigated individual temporal patterns of user check-in behavior on LBSNs

Propose a location recommendation framework with temporal e ects and evaluate it on a real-world datasetff

Future Work Explore other temporal patterns (e.g., monthly/ yearly

patterns)

Study the complementary effects of different kind of temporal patterns