Learning Graph-based POI Embedding for Location-based...
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Learning Graph-based POI Embedding for Location-based Recommendation Min Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, Sen Wang Institute of Software, Chinese Academy of Sciences, China The University of Queensland, Australia
Transcript of Learning Graph-based POI Embedding for Location-based...
Learning Graph-based POI Embedding for Location-based Recommendation
Min Xie, Hongzhi Yin, Hao Wang,
Fanjiang Xu, Weitong Chen, Sen Wang
Institute of Software, Chinese Academy of Sciences, China
The University of Queensland, Australia
Outline
1 Location-based
Recommendation
Experiments
Graph-based
Embedding Model
Conclusions
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2
3 4
Outline
1
2018/7/17 3
2
3 4
Location-based
Recommendation
Graph-based
Embedding Model
ConclusionsExperiments
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Social Networks (SNs)
2004 - 2010
VIR
TU
AL
WO
RL
D
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Location-based Social Networks (LBSNs)
2010 - …
VIR
TU
AL
WO
RL
DP
HY
SIC
AL
WO
RL
D
Mobile communication + Positioning technologies
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Location-based Recommendation
To recommend points-of-interest (POIs) that a user is interested in but has not visited
To users: discovering new places, knowing their cities better
To merchants: launch mobile advertisements to targeted users.
Given a user 𝒖 with his/her current location 𝒍 and time 𝝉， recommend top-k POIs that 𝒖 would be interested in.
User 𝒖
Current location
Current time
User preference
Data Sparsity
Physically visit a POI is more expensive than rating a movie online.
Privacy or safety concerns.
Context Awareness
Not only considering personal preferences, but also the spatiotemporal context.
User tends to have different choices and needs at different time and places.
Cold Start
New POIs (e.g., business) are emerging every day.
Dynamic of Personal Preferences
Users’ preferences are changing with the time going on.
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Challenges
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How to overcome?
Sequential
Effect
Geographical
Influence
Temporal
Cyclic Effect
Semantic
Effect
Exploit and integrate multi factors in a unified way.
Sequential
Effect
Geographical
Influence
Temporal
Cyclic Effect
Semantic
Effect
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Integrate Multi Factors
Transition probabilities from one checked-in POI to other POIs is a
non-uniform distribution.
Sequential
Effect
Geographical
Influence
Temporal
Cyclic Effect
Semantic
Effect
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Integrate Multi Factors
People tend to visit nearby POIs or explore POIs near the ones that
they have visited before.
Sequential
Effect
Geographical
Influence
Temporal
Cyclic Effect
Semantic
Effect
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Integrate Multi Factors
Users’ mobility behaviors exhibit strong temporal cyclic patterns, and
the daily pattern (hours of the day).
Sequential
Effect
Geographical
Influence
Temporal
Cyclic Effect
Semantic
Effect
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Integrate Multi Factors
Contents of POIs checked-in by the same user tend to semantically similar.
Outline
1
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2
3 4
Graph-based
Embedding Model
ConclusionsExperiments
Location-based
Recommendation
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Graph-based Embedding Model
We introduce four kinds of graphs to model Sequential Effect, geographical influence, temporal cyclic effect and semantic effect, respectively.
POI-POI Graph captures the sequential patterns of check-in POIs. If 𝑣𝑖 and 𝑣𝑗are often checked in sequentially, their correlation will be larger.
POI-Region Graph captures the geographical influence. If POI 𝑣𝑖 is located in region 𝑟𝑗, there will be an edge 𝑒𝑖𝑗 between them.
POI-Time Graph captures the temporal cyclic effect. The weight 𝑤𝑖𝑗 of the edge
between POI 𝑣𝑖 and time slot 𝑡𝑗 is defined as the frequency of POI 𝑣𝑖 checked in at
time slot 𝑡𝑗 .
POI-Word Graph captures the semantic effect. If word 𝑤𝑖 can describe POI 𝑣𝑗, there will be an edge 𝑒𝑖𝑗 between them.
Goal: to embed the above four graphs into a shared low dimensional space ℝ𝑑, and get the vector representations of POIs, regions, time
slots and words, i.e., Ԧ𝑣, Ԧ𝑟, Ԧ𝑡 and 𝑤.
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Model Structure of GE
POI_1
region_1
region_2
region_3
POI_2
POI_3
POI_4
POI_5
POI_6
(a) POI-POI Graph
POI_1
POI_2
POI_3
POI_4
...
...
...
...
(b) POI-Region Graph
POI_1
POI_2
POI_3
POI_4
...
...
time slot_1
time slot_2
...
...
(c) POI-Time Graph
POI_1
POI_2
POI_3
POI_4
...
...
word_1
word_2
...
...
word_3
word_4
word_5
(d) POI-Word Graph
Get the vector representations of POIs, regions, time slots and words, i.e.,
Ԧ𝑣, Ԧ𝑟, Ԧ𝑡 and 𝑤.
Dynamic User Preference Modeling:
Recommendation Using GE
𝑢𝜏 =
(𝑢,𝑣𝑖,𝜏𝑖)∈𝐷𝑢⋂(𝜏𝑖<𝜏)
𝑒𝑥𝑝−(𝜏−𝜏𝑖) ⋅ Ԧ𝑣𝑖
𝑆 𝑞, 𝑣 = 𝑢𝜏𝑇 ∙ Ԧ𝑣 + Ԧ𝑟𝑇 ∙ Ԧ𝑣 + Ԧ𝑡𝑇 ∙ Ԧ𝑣
(1)
(2)
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Optimization
All the graphs share the vector representations of POIs.
Optimizing four graphs in turn, and update vector representations.
Bipartite Graph Embedding
Joint Embedding Learning
𝑂 = −
𝑒𝑖𝑗∈𝜀
𝑤𝑖𝑗 𝑙𝑜𝑔𝑝(𝑣𝑗|𝑣𝑖)
𝑂 = 𝑂𝑣𝑣 + 𝑂𝑣𝑟+𝑂𝑣𝑡+𝑂𝑣𝑤
𝑂𝑣𝑣 = −
𝑒𝑖𝑗∈𝜀𝑣𝑣
𝑤𝑖𝑗 𝑙𝑜𝑔𝑝(𝑣𝑖|𝑣𝑗) 𝑂𝑣𝑟 = −
𝑒𝑖𝑗∈𝜀𝑣𝑟
𝑤𝑖𝑗 𝑙𝑜𝑔𝑝(𝑣𝑖|𝑟𝑗)
𝑂𝑣𝑡 = −
𝑒𝑖𝑗∈𝜀𝑣𝑡
𝑤𝑖𝑗 𝑙𝑜𝑔𝑝(𝑣𝑖|𝑡𝑗) 𝑂𝑣𝑤 = −
𝑒𝑖𝑗∈𝜀𝑣𝑤
𝑤𝑖𝑗 𝑙𝑜𝑔𝑝(𝑣𝑖|𝑤𝑗)
(3)
(4)
Outline
1
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2
3 4
Graph-based
Embedding Model
ConclusionsExperiments
Location-based
Recommendation
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Experiments
Datasets
On two real large-scale LBSNs datasets: Foursquare and Gowalla.
Comparison Approaches
SVDFeature. Beyond user-POI matrix, implement it by incorporating POI content, POI geographical location and check-in time.
JIM. It strategically integrates semantic effect, temporal effect, geographical influence and word-of-mouth effect
PRME-G. Based on embedding techniques, but utilized two latent spaces: sequential transition space and user preferences space.
Geo-SAGE. Exploits POI content information and the crowd’s preference at a region.
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Evaluation
Split the dataset into the training set 𝐷𝑡rain and test set 𝐷𝑡𝑒𝑠𝑡 according to their check-in timestamps.
Accuracy@k evaluation
hit@k for a single test case as either the value 1, if the ground truth POI v appears in the top-k results, or the value 0.
k = {1, 5, 10, 15, 20}
Accuracy@k =#ℎ𝑖𝑡@𝑘
|𝐷𝑡𝑒𝑠𝑡|
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Recommendation Effectiveness
GE model outperforms other competitor models significantly.
GE and PRME-G achieves much higher recommendation accuracy than
other comparison methods in top-1 recommendation
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Variants of GE
Impact of Different Factors
GE-S1 Without sequential effect
GE-S2 No POI-Region graph
GE-S3 No POI-Time graph
GE-S4 No POI-Word graph
Sequential Effect > Temporal Effect > Content Effect > Geographical Influence.
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Variants of GE
Exploring Various Temporal Patterns
GE Daily pattern (24 hours of a day)
GE-S5 Weekly pattern (day of the week)
GE-S6 Weekday/weekend pattern
GE-S3 No POI-Time graph
Exploiting daily pattern, weekly pattern or weekday/weekend pattern can
largely improve performance
Improvement brought by exploiting daily pattern is the most significant.
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Test for Cold Start Problem
Cold start POIs: no check-in information, that is no user has checked
in there.
PRME-G model does not work in the cold-start scenario.
GE model still performs best.
Accuracy of GE model deteriorate slightly.
Outline
1
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2
3 4
Graph-based
Embedding Model
ConclusionsExperiments
Location-based
Recommendation
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Conclusions
We are the first to investigate the joint effect of sequential effect, geographical influence, temporal cyclic effect and semantic effect.
We develop a graph-based embedding model to learn the representations of POIs, time slots, geographical regions and content words in a shared low-dimension space.
We propose a novel method for dynamic user preferences modeling based on the learnt embedding of POIs, to support real-time recommendation.
We conduct extensive experiments to evaluate the performance of our recommender method on two real large-scale datasets, and verified its effectiveness.
Moreover, we found that both sequential effect and temporal cyclic effect play a dominant role in location-based recommendation and the daily pattern is the most significant temporal cyclic pattern in users’ daily behaviors.
