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Point-of-Interest Recommendations: Learning Potential Check-ins from Friends
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Huayu Li∗, Yong Ge+, Richang Hong−, Hengshu Zhu×
∗University of North Carolina at Charlotte
+University of Arizona
−Hefei University of Technology×
Baidu Research-Big Data Lab
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Outline
Introduction
Research Problem
Research Challenges
Related Work
Methodologies
Experiments
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Introduction
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Users Mobile DevicesLocation-based Social
Network (LBSN) Services
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Introduction
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Introduction
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Introduction
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Introduction
Information Overload• Foursquare: 65 million venues
• Facebook: 16 million local business
• Yelp: 2.1 million claimed business
New Region
Which One?
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Introduction
Information Overload• Foursquare: 65 million venues
• Facebook: 16 million local business
• Yelp: 2.1 million claimed business
New Region
Which One? A location recommender system is very important!
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Research Problem
9
Given a set of users and a set of locations they have visited
before, the objective is to recommend the locations to an
individual who might have interest to visit.
visited recommended
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Research Challenges
Complex Decision Making Process• Social Network Influence
• Geographical Influence
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Research Challenges
Complex Decision Making Process• Social Network Influence
• Geographical Influence
Data Sparsity Issue• Each user only visits a limited number of locations.
• For new user/location, we do not have their check-in information.
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Research Challenges
Complex Decision Making Process• Social Network Influence
• Geographical Influence
Data Sparsity Issue• Each user only visits a limited number of locations.
• For new user/location, we do not have their check-in information.
Implicit Feedback Issue• Only check-in frequency without explicit rating.
• We do not know user’s explicit preference for locations.
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Related Work
Modeling Social Network Influence• Social regularization constraint (WSDM’11)
• Social correlations (CIKM’12, IJCAI’13, ICDM’15)
• User-based collaborative filtering (SIGIR’11)
Modeling geographical influence• Incorporating geographical distance (KDD’11, SIGIR’11, AAAI’12,
SIGSPATIAL’ 13, KDD’14, ICDM’15)
• Incorporating activity area (KDD’ 14)
• Incorporation nearest neighbors (CIKM’14)
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Methods: Framework
Learn potential locations from
friends
Learn user’s preference for
locations
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Methods: Framework
Learn potential locations from
friends
Learn user’s preference for
locations
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Definition of Friends
Social Friends ℱ𝑖s
• The users who socially connect with the target user 𝑖 in LBSNs.
Location Friends ℱ𝑖𝑙
• The users who check-in the same locations as the target user 𝑖.
Neighboring Friends ℱ𝑖𝑛
• The users who live physically closest to the target user 𝑖.
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𝑙1𝑙2
𝑙3𝑙4 𝑙5
𝑓1
𝑓2
𝑓3
𝑓4
𝑓5
𝑓6
𝑢𝑖
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Definition of Friends
Social Friends ℱ𝑖s
• The users who socially connect with the target user 𝑖 in LBSNs.
Location Friends ℱ𝑖𝑙
• The users who check-in the same locations as the target user 𝑖.
Neighboring Friends ℱ𝑖𝑛
• The users who live physically closest to the target user 𝑖.
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𝑙1𝑙2
𝑙3𝑙4 𝑙5
𝑓1
𝑓2
𝑓3
𝑓4
𝑓5
𝑓6
𝑢𝑖
ℱi = ℱ𝑖s ∪ 𝑆(ℱ𝑖
𝑙) ∪ 𝑆(ℱ𝑖𝑛)
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Methods: Learning Potential Locations
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𝑢𝑖PROBLEM DEFINITION:For the target user 𝑖, given
a set of locations that her
friends have checked-in
before but she never visits,
the problem is to find top
most potential locations that
she might be interested in.
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Methods: Learning Potential Locations
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𝑢𝑖
𝑃𝑖𝑗𝑝𝑜𝑡
?
𝑙𝑗
Location Candidate
Linear Aggregation
Random Walk
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Methods: Linear Aggregation
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𝑢𝑖
𝑙𝑗
Probability 𝑃𝑖𝑗𝑝𝑜𝑡
that
user 𝑖 visits a location 𝑗:
𝑃𝑖𝑗𝑝𝑜𝑡
∝ max𝑓∈ℱ
𝑖𝑗{𝑆𝑖𝑚(𝑖, 𝑓; 𝑗)}
𝜁𝑆𝑖𝑚𝑢 𝑖, 𝑓 + (1 − 𝜁)𝑃𝑖𝑗𝐺
Similarity of User Interest Similarity of Geo-location
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Methods: Random Walk
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𝑢𝑖 Nodes: users and locations
Links: user-user, user-location, location-location
𝐲 = 1 − 𝛽 𝐀𝐲 +𝛽
|ℳ𝑖𝑜∩ℳ𝑖
𝑓|+|ℱ𝑖|+1
x
𝑃𝑖𝑗𝑝𝑜𝑡
is the steady probability
corresponding to location j
Transition Matrix Restart Nodes
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Methods: Learning Potential Locations
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Observed Locations Potential Locations Other Unobserved Locations
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Methods: Framework
Learn potential locations from
friends
Learn user’s preference for
locations
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Recommendation Models
The preference Ƹ𝑝𝑖𝑗 of user 𝑖 for location 𝑗:
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≈×d
⊙Users’ preference for locations
Category FeatureMatrix
Location Latent Matrix
User Latent Matrix
Ƹ𝑝𝑖𝑗 = (𝑞𝑖𝑐𝑗 + 𝜀) 𝐮𝑖𝑇𝐯𝑗
User’s Preference for Category
Tuning Parameter
User’s Typical Preference for Location
𝐏 ෩𝐐 = 𝐐 + 𝛆
𝐔
𝐕
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Recommendation Models
Loss function of general form
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argmin𝐔,𝐕,𝐐
𝑖
𝐸𝑖 𝑝𝑖𝑗 , 𝑝𝑖𝑘 , 𝑝𝑖ℎ , ො𝑝𝑖𝑗 , ො𝑝𝑖𝑘 , ො𝑝𝑖ℎ + Θ(𝐔, 𝐕,𝐐)
∀ 𝑗 ∈ ℳ𝑖𝑜, 𝑘 ∈ ℳ𝑖
𝑝, ℎ ∈ ℳ𝑖
𝑢
Estimated Value
Observed Locations
Potential Locations
Other Unobserved Locations
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Recommendation Models
Loss function of general form
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argmin𝐔,𝐕,𝐐
𝑖
𝐸𝑖 𝑝𝑖𝑗 , 𝑝𝑖𝑘 , 𝑝𝑖ℎ , ො𝑝𝑖𝑗 , ො𝑝𝑖𝑘 , ො𝑝𝑖ℎ + Θ(𝐔, 𝐕,𝐐)
∀ 𝑗 ∈ ℳ𝑖𝑜, 𝑘 ∈ ℳ𝑖
𝑝, ℎ ∈ ℳ𝑖
𝑢
Estimated Value
Observed Locations
Potential Locations
Other Unobserved Locations
𝜆𝑢
2||𝐔||2
2 +𝜆𝑣
2||𝐕||2
2+𝜆𝑞
2||𝐐||2
2
Regularization Term
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Recommendation Models
Loss function of general form
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argmin𝐔,𝐕,𝐐
𝑖
𝐸𝑖 𝑝𝑖𝑗 , 𝑝𝑖𝑘 , 𝑝𝑖ℎ , ො𝑝𝑖𝑗 , ො𝑝𝑖𝑘 , ො𝑝𝑖ℎ + Θ(𝐔, 𝐕,𝐐)
∀ 𝑗 ∈ ℳ𝑖𝑜, 𝑘 ∈ ℳ𝑖
𝑝, ℎ ∈ ℳ𝑖
𝑢
Observed Locations
Potential Locations
Other Unobserved Locations
𝜆𝑢
2||𝐔||2
2 +𝜆𝑣
2||𝐕||2
2+𝜆𝑞
2||𝐐||2
2
Regularization Term
Square Error based Model Ranking Error based Model
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Square Error based Model
The user’s preference for a location is defined as:
𝑝𝑖𝑗 = ൞
1 𝑖𝑓 𝑗 ∈ ℳio
𝛼 𝑖𝑓 𝑗 ∈ ℳi𝑝
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
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Observed Locations
Potential Locations
Other unobserved Locations
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Square Error based Model
The user’s preference for a location is defined as:
𝑝𝑖𝑗 = ൞
1 𝑖𝑓 𝑗 ∈ ℳio
𝛼 𝑖𝑓 𝑗 ∈ ℳi𝑝
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Squared error loss function
𝐸𝑖 ∙ =
𝑗=1
𝑀
𝑤𝑖𝑗(𝑝𝑖𝑗 − Ƹ𝑝𝑖𝑗 )2
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𝑤𝑖𝑗 = ቊ1 + 𝛾 × 𝑟𝑖𝑗 , 𝑖𝑓 𝑗 ∈ ℳi
o
1, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Weight Matrix
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Square Error based Model
Squared error based objective function
ℒ
= min𝐔,𝐕,𝐐
𝑖=1
𝑁
𝑗=1
𝑀
𝑤𝑖𝑗(𝑝𝑖𝑗 − Ƹ𝑝𝑖𝑗 )2
+ Θ(𝐔, 𝐕, 𝐐)
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Initialization
Alternating Update
Alternating Least Square
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Ranking Error based Model
Model the ranking order among user’s preference for three types of locations
ቊƸ𝑝𝑖𝑗 > Ƹ𝑝𝑖𝑘Ƹ𝑝𝑖𝑘 > Ƹ𝑝𝑖ℎ
, ∀ 𝑗 ∈ ℳ𝑖𝑜,𝑘 ∈ ℳ𝑖
𝑝, ℎ ∈ ℳ𝑖
𝑢
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Observed Location
Potential Location
Potential Location
Other Unobserved Location
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Ranking Error based Model
Model the ranking order among user’s preference for three types of locations
ቊƸ𝑝𝑖𝑗 > Ƹ𝑝𝑖𝑘Ƹ𝑝𝑖𝑘 > Ƹ𝑝𝑖ℎ
, ∀ 𝑗 ∈ ℳ𝑖𝑜,𝑘 ∈ ℳ𝑖
𝑝, ℎ ∈ ℳ𝑖
𝑢
Ranking error loss function
𝐸𝑖 ∙ = −
𝑗∈ℳ𝑖𝑜
𝑘∈ℳ𝑖𝑝
ln 𝜎( Ƹ𝑝𝑖𝑗 − Ƹ𝑝𝑖𝑘) −
𝑘∈ℳ𝑖𝑝
ℎ∈ℳ𝑖𝑢
ln 𝜎( Ƹ𝑝𝑖𝑘 − Ƹ𝑝𝑖ℎ)
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Using Logistic Function to Model Ranking Order
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Ranking Error based Model
Ranking error based objective function
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Initialization
Update
Stochastic Gradient Descent with Boostrap Sampling
Sampling
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Incorporating Geographical Influence
Check-in probability is refined by a power-law function associated with the distance between user home position and a location.
Ƹ𝑝𝑖𝑗 ∝ 𝑝𝑖𝑗𝐺 × 𝜎( Ƹ𝑝𝑖𝑗)
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𝑝𝑜𝑤𝑒𝑟𝑙𝑎𝑤(𝑑(𝑖, 𝑗))
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Recommendation Strategies
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Target User 𝑖
New Location
Standard Recommendation
New User RecommendationƸ𝑝𝑖𝑗 = (𝑞𝑖𝑐𝑗 + 𝜀) 𝐮𝑖
𝑇𝐯𝑗
New Location Recommendation
Ƹ𝑝𝑖𝑗 ∝ 𝑝𝑖𝑗𝐺× 𝜎
σ𝑙∈𝜓𝑗
𝑆𝑖𝑚𝐺(𝑗, 𝑙) Ƹ𝑝𝑖𝑙
σ𝑙∈𝜓𝑗
𝑆𝑖𝑚𝐺(𝑗, 𝑙)
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Datasets: Gowalla
Test Methodology• Selecting 80% as training and using the rest 20% as testing according to
timestamp
Evaluation Metrics: • Top-K Recommendation Accuracy
(Precision@K and Recall@K)
Experiments
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Statistics of Data Set
New Location Rec New User Rec
#User #Location #Check-in Sparsity #New Location #Test #New User #Test
52,216 98,351 2,577,336 0.0399% 78,881 568,937 9,326 79,153
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Exp. : Standard Recommendation
37
Precision@K Recall@K
Modeling unobserved check-ins can improve recommendation accuracy !
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Exp. : Standard Recommendation
38
Precision@K Recall@K
Modeling potential check-ins can benefit recommendation!
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Exp. : New User Recommendation
39
Precision@K Recall@K
Modeling potential check-ins can solve user cold-start issue!
![Page 40: Point-of-Interest Recommendations: Learning …hli38/slide/KDD_16.pdfPoint-of-Interest Recommendations: Learning Potential Check-ins from Friends 1 HuayuLi∗, YongGe+, RichangHong−,](https://reader036.vdocuments.mx/reader036/viewer/2022070718/5ede2936ad6a402d6669759f/html5/thumbnails/40.jpg)
Exp. : New Location Recommendation
40
Modeling potential check-ins can solve location cold-start issue!
Performance comparison for new location recommendation in terms of Precision@K and Recall@K.
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Conclusion
Empirically analyze the correlations between users and their three type of friends using real-world data
Learn a set of locations for each user that her friends have checked-in before and she is most interested in
Develop matrix factorization based models via different error loss functions with the learned potential check-ins, and propose two scalable optimization methods
Design three different recommendation strategies
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42
Thank You