quan yuan, gao cong, zongyang ma, aixin sun, and nadia ... · as of january 2013, foursquare had...
TRANSCRIPT
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Time-aware Point-of-interest Recommendation
Quan Yuan, Gao Cong, Zongyang Ma,Aixin Sun, and Nadia Magnenat-Thalmann
School of Computer EngineeringNanyang Technological University
Presented by ShenglinZHAO (CUHK) SIGIR 2013 1 / 24
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Outline
1 Introduction
2 Related Work
3 ModelsUtilizing Temporal InfluenceUtilizing Spacial InfluenceA Unified Framework
4 ExperimentMetrics & DataResultsDiscussion
5 Conclusion & Further Work
6 Insights
Presented by ShenglinZHAO (CUHK) SIGIR 2013 2 / 24
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Introduction
Introduction
Presented by ShenglinZHAO (CUHK) SIGIR 2013 3 / 24
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Introduction
Introduction
As of January 2013, Foursquare had over 3 billion check-ins made by 30million users.
Figure 1 : An example of check-in
Presented by ShenglinZHAO (CUHK) SIGIR 2013 4 / 24
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Introduction
Introduction
A point of interest (POI) is aspecific point location thatsomeone may find interestingand be willing to check in.
Objective of POIrecommendation:discover newplaces
Presented by ShenglinZHAO (CUHK) SIGIR 2013 5 / 24
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Introduction
Introduction
How to recommend a point-of-interest?
User-based collaborative filtering method performs well [Ye et al., 2011b]
Problem of existing methods:
No existing work has considered the time factor for POI recommendationsin LBSNs.
Proposal:
Explore users’ temporal behavior and define a new time-aware POIrecommendation problem;Further, study users’ spacial behavior and employ a unified POIrecommendation framework.
Presented by ShenglinZHAO (CUHK) SIGIR 2013 6 / 24
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Introduction
Introduction
Contributions
Define a new time-aware POI recommendation problem
Fuse the spacial and temporal influences with a framework to makethe time-aware POI recommendation
Conduct experiments on real-world LBSN datasets and demonstratethat time has significant influence and the proposed models performbetter
Presented by ShenglinZHAO (CUHK) SIGIR 2013 7 / 24
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Related Work
Related Work
Collaborative Filtering
[Koren et al., 2009], [Ding and Li, 2005], [Su and Khoshgoftaar, 2009]
POI Recommendation & POI Prediction
[Ye et al., 2011b], [Ye et al., 2010], [Cheng et al., 2012],[Cho et al., 2011],[Clements et al., 2010]
Location Identification and Recommendation
[Zheng et al., 2009], [Leung et al., 2011], [Cao et al., 2010]
Recommendation with Temporal Information
[Ye et al., 2011a], [Ding and Li, 2005], [Xiang et al., 2010]
Contextual-aware Recommendation
[Adomavicius et al., 2005]
Presented by ShenglinZHAO (CUHK) SIGIR 2013 8 / 24
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Models
Models
Utilizing Temporal Influence
Utilizing Spacial Influence
Unified Framework
Presented by ShenglinZHAO (CUHK) SIGIR 2013 9 / 24
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Models Utilizing Temporal Influence
User-based Collaborative Filtering
UCF in formula:
cu,l =
∑v wu,vcv ,l∑v wu,v
where cu,l denotes the score that u will check-in a POI l , wu,v is thesimilarity between user u and user v .
Notes
Let cv ,l = 1 if v has checked in l ; and cv ,l = 0 otherwise.
Presented by ShenglinZHAO (CUHK) SIGIR 2013 10 / 24
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Models Utilizing Temporal Influence
Incorporating Temporal Influence
Check-in Representation
user-POI matrix → user-time-POI cube (UTP)
Recommendation Formula
cu,l =∑
v wu,vcv ,l∑v wu,v
→ cu,t,l =∑
v w(t)u,vcv ,t,l∑v w
(t)u,v
Similarity Estimation
wu,v =∑
l cu,lcv,l√∑l c
2u,l
√∑l c
2v,l
→ w(t)u,v =
∑Tt=1
∑Ll=1 cu,t,lcv,t,l√∑T
t=1
∑Ll=1 c
2u,t,l
√∑Tt=1
∑Ll=1 c
2v,t,l
Presented by ShenglinZHAO (CUHK) SIGIR 2013 11 / 24
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Models Utilizing Temporal Influence
Enhancement by Smoothing
Drawback of aforementioned method: Sparsity
Example
User u checks in l1 and l2 at t1 and t2; while user v checks in l1 and l2 att2 and t1.
Similarity between u and v with temporal influence: 0
Similarity between u and v without temporal influence: 1
Proposal: Smoothing by time slot similarity
Formulation
cu,t,l =T∑
t′=1
ρt,t′∑Tt′′=1 ρt,t′′
cu,t′,l
w(t)u,v =
∑Tt=1
∑Ll=1 cu,t,l cv ,t,l√∑T
t=1
∑Ll=1 c
2u,t,l
√∑Tt=1
∑Ll=1 c
2v ,t,l
Presented by ShenglinZHAO (CUHK) SIGIR 2013 12 / 24
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Models Utilizing Spacial Influence
Incorporating Spacial Influence
Observation
Power law distribution: wi(dis) = a ∗ disk
Conditional probability
p(lj |li ) =wi(dis(li ,lj ))∑
lk∈L,lk 6=liwi(dis(li ,lk ))
Recommend by spacial influence
c(s)u,l = P(l |Lu) ∝ P(l)P(Lu|l) = P(l)
∏l ′∈Lu P(l ′|l)
Presented by ShenglinZHAO (CUHK) SIGIR 2013 13 / 24
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Models Utilizing Spacial Influence
Enhancement by Temporal Popularity
Temporal Popularity
The probability of checking in a POI should reflect both its popularity atthe specific time and the distance to the user’s current location.
Pt(l) = β|CIl ,t |∑
l ′∈L |CIl ′,t |+ (1− β)
|CIl |∑l ′∈L |CIl ′ |
,
where CIl is the number of check-ins at l , |CIl ,t | is the number ofcheck-ins at l at time t, and beta is the weighting parameter.
Enhanced by temporal popularity
c(se)u,t,l = Pt(l)
∏l ′∈Lu P(l ′|l)
Presented by ShenglinZHAO (CUHK) SIGIR 2013 14 / 24
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Models A Unified Framework
Unified Framework
Linear combination:
cu,t,l = α× c(t)u,t,l + (1− α)× c
(s)u,t,l
where cu,t,l denotes the score that user u will check in POI l at time t,
c(t)u,t,l and c
(s)u,t,l denote the score from temporal influence and spacial
influence respectively.
Notes
c(t)u,t,l and c
(s)u,t,l are normalized by min-max method.
Presented by ShenglinZHAO (CUHK) SIGIR 2013 15 / 24
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Experiment Metrics & Data
Experimental Setup
Metrics: Accuracy of POI recommendation (precision & recall)
Data: Two datasets from Foursquare and Gowalla
Table 1 : Data statistics (after pre-processing)
Dataset No. of Check-ins No. of Users No. of POIsFoursqaure 194,108 2,321 5,596
Gowalla 456,988 10,162 24,250
Presented by ShenglinZHAO (CUHK) SIGIR 2013 16 / 24
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Experiment Results
Methods for Comparison
U: User-based CF [Ye et al., 2011b]
UTF: U with Time Function [Ding and Li, 2005]
UT: U with Temporal preference
UTE: UT with smoothing Enhancement
SB: Spacial influence based Baseline [Ye et al., 2011b]
S: Spacial influence based recommendation
SE: S with popularity Enhancement
U+SB: Combination of U and SB [Ye et al., 2011b]
UTE+SE: Combination of UTE and SE
Presented by ShenglinZHAO (CUHK) SIGIR 2013 17 / 24
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Experiment Results
Results
Figure 2 : Performance of Methods Utilizing Temporal InfluencePresented by ShenglinZHAO (CUHK) SIGIR 2013 18 / 24
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Experiment Results
Results
Figure 3 : Performance of Methods Utilizing Spacial InfluencePresented by ShenglinZHAO (CUHK) SIGIR 2013 19 / 24
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Experiment Results
Results
Figure 4 : Performance of Unified MethodsPresented by ShenglinZHAO (CUHK) SIGIR 2013 20 / 24
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Experiment Discussion
Discussion—Effect of the Length of Time Slot
Figure 5 : Performance of varying length of time slot
Presented by ShenglinZHAO (CUHK) SIGIR 2013 21 / 24
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Experiment Discussion
Discussion—Case Study
Figure 6 : Performance of different time of a day
Presented by ShenglinZHAO (CUHK) SIGIR 2013 22 / 24
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Conclusion & Further Work
Conclusion & Further Work
Conclusion
First work on time-aware POI recommendations.
Propose a new approach exploring the spacial influence.
Experimental results show that the proposed methods beat allbaselines, and improve the accuracy of POI recommendations by morethan 37% over the state-of-the-art method.
Further work
Exploit other time dimensions in POI recommendations, e.g., the dayof a week.
Exploit category information in POI recommendations.
Presented by ShenglinZHAO (CUHK) SIGIR 2013 23 / 24
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Insights
Some insights from me
1 Good writing
2 Clarity
3 Details
Presented by ShenglinZHAO (CUHK) SIGIR 2013 24 / 24