Temporal recommendation on
graphs via long- and short-term
preference fusion
Liang Xiang
xlvector@gmail.com
Main Content
• Temporal Recommendation
– Long/short term preference
• Bipartite Graph Model
– Session Graph Model
– Path F...
Related Works
• Neighborhood Model [Ding CIKM05]
– Users future preference is mainly dependent on
their recent behavior
• ...
Our Contribution
• Temporal Recommendation on Graph Model
– Implicit feedback data
• Combine Long/short term interest toge...
Long/Short Term Preference
Short-term Preference
Long-term Preference
Long/Short Term Preference
• Long term preference
– Personal preference
– Do not change frequently
– Last for long period
...
Session Graph Model
Session Graph Model
A
B
a
b
c
(A,a,1) (A,c,2)
(B,b,1) (B,c,2)
A
B
a
b
c
A:1
A:2
B:1
B:2
Bipartite Graph Model Session Grap...
Session Graph Model
Session Node
User
Node
Item Node
1

1
1
1
( )
(1 )
i
u
uT
v v
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 pat...
Path Fusion Ranking
A
B
a
b
c
1
1
1
( ) ( , )
( )
| ( ) |
N
i i i
i i
v w v v
weight P
out v 



 
( , ')
( , ') ( ...
Path Fusion Ranking
1. Implement by Breath-First-Search
2. Fast and low space complexity
a) Its speed dependents on graph
...
Experiments
Experiments
Experiments
This model does not work in
every system!
Future work
Temporal Effectiveness
Slow Evolution System
Session Graph Model Perform Good
Fast Evolution System
Session Graph Model Pe...
Temporal Effectiveness
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50 60
nytimes youtube wikipedia sourceforge b...
Solution
• Add Item Session Node
A
B
a
b
c
A
B
a
b
c
A:1
A:2
B:1
B:2
A
B
a
b
c
A:1
A:2
B:1
B:2
a:1
b:1
c:2
(A,a,1) (A,c,2)...
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Temporal recommendation on graphs via long and short-term

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Temporal recommendation on graphs via long and short-term

  1. 1. Temporal recommendation on graphs via long- and short-term preference fusion Liang Xiang xlvector@gmail.com
  2. 2. Main Content • Temporal Recommendation – Long/short term preference • Bipartite Graph Model – Session Graph Model – Path Fusion Algorithm
  3. 3. 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
  4. 4. Our Contribution • Temporal Recommendation on Graph Model – Implicit feedback data • Combine Long/short term interest together Graph Model Temporal Recommendation
  5. 5. Long/Short Term Preference Short-term Preference Long-term Preference
  6. 6. 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
  7. 7. Session Graph Model
  8. 8. Session Graph Model A B a b c (A,a,1) (A,c,2) (B,b,1) (B,c,2) A B a b c A:1 A:2 B:1 B:2 Bipartite Graph Model Session Graph Model Session Node User Node Item Node
  9. 9. Session Graph Model Session Node User Node Item Node 1  1 1 1 ( ) (1 ) i u uT v v v v v v v          
  10. 10. Ranking and Recommendation
  11. 11. 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]
  12. 12. Path Fusion Ranking A B a b c 1 1 1 ( ) ( , ) ( ) | ( ) | N i i i i i v w v v weight 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 b weight A c B b         
  13. 13. Path Fusion Ranking 1. Implement by Breath-First-Search 2. 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 update a) New data can insert into graph directly; b) After graph is updated, recommendation result will be changed immediately
  14. 14. Experiments
  15. 15. Experiments
  16. 16. Experiments
  17. 17. This model does not work in every system! Future work
  18. 18. Temporal Effectiveness Slow Evolution System Session Graph Model Perform Good Fast Evolution System Session Graph Model Perform Bad
  19. 19. Temporal Effectiveness 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 10 20 30 40 50 60 nytimes youtube wikipedia sourceforge blogspot netflix
  20. 20. Solution • Add Item Session Node A B a b c A B a b c A:1 A:2 B:1 B:2 A B a b c 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)
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