Temporal recommendation on graphs via long and short-term
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Temporal recommendation on graphs via long- and short-term preference fusion Liang Xiang [email protected]
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Transcript of Temporal recommendation on graphs via long and short-term
Temporal recommendation on graphs via long- and short-term preference fusion
Liang [email protected]
Main Content
• Temporal Recommendation– Long/short term preference
• Bipartite Graph Model– Session Graph Model– Path Fusion Algorithm
Related Works
• Neighborhood Model [Ding CIKM05]– Users future preference is mainly dependent on
their recent behavior• Latent Factor Model [Koren KDD09]– User bias shifting– Item bias shifting– User preference shifting– Seasonal effects
Our Contribution
• Temporal Recommendation on Graph Model– Implicit feedback data
• Combine Long/short term interest together
Graph Model Temporal Recommendation
Long/Short Term Preference
Short-term PreferenceLong-term Preference
Long/Short Term Preference
• Long term preference– Personal preference– Do not change frequently– Last for long period
• Short term preference– Influenced by social event– Change frequently– May be become long term preference
Session Graph Model
Session Graph Model
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B
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(A,a,1) (A,c,2)(B,b,1) (B,c,2)
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A:1
A:2
B:1
B:2
Bipartite Graph Model Session Graph Model
Session Node
User Node
Item Node
Session Graph Model
Session Node
User Node
Item Node
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( )
(1 )
i
u
uT
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v v v
v v
Ranking and Recommendation
Path Fusion Ranking
• Two nodes in a graph have large similarity if:– There are many paths between two nodes;– These paths have short length;– Most of these paths do not contains nodes with
large out degree.
[YouTube WWW2008]
Path Fusion Ranking
A
B
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1
( ) ( , )( )
| ( ) |
Ni i i
i i
v w v vweight P
out v
( , ')
( , ') ( )P path v v
d v v weight P
( ) ( , ) ( ) ( , ) ( ) ( , )( , , , )
| 2 | | 2 | | 2 |
A w A c c w c B B w B bweight A c B b
Path Fusion Ranking1. Implement by Breath-First-Search2. Fast and low space complexity
a) Its speed dependents on graph sparsity;
b) It can be speed up by randomly select edges;
c) Do not need to store user-user or item-item similarity matrix
3. Easy to do incremental updatea) New data can insert into graph
directly;b) After graph is updated,
recommendation result will be changed immediately
Experiments
Experiments
Experiments
This model does not work in every system!
Future work
Temporal Effectiveness
Slow Evolution SystemSession Graph Model Perform Good
Fast Evolution SystemSession Graph Model Perform Bad
Temporal Effectiveness
0 10 20 30 40 50 600
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nytimes youtube wikipediasourceforge blogspot netflix
Solution
• Add Item Session Node
A
B
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A
B
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A:1
A:2
B:1
B:2
A
B
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A:1
A:2
B:1
B:2
a:1
b:1
c:2
(A,a,1) (A,c,2)(B,b,1) (B,c,2)
