1. Temporal recommendation on
graphs via long- and short-term
preference fusion
Liang Xiang
xlvector@gmail.com

2. Main Content
• Temporal Recommendation
– Long/short term preference
• Bipartite Graph Model
– Session Graph Model
– Path Fusion Algorithm

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. Our Contribution
• Temporal Recommendation on Graph Model
– Implicit feedback data
• Combine Long/short term interest together
Graph Model
Temporal
Recommendation

5. Long/Short Term Preference
Short-term Preference
Long-term Preference

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. Session Graph Model

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. 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. Ranking and Recommendation

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. 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. 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. Experiments

15. Experiments

16. Experiments

17. This model does not work in
every system!
Future work

18. Temporal Effectiveness
Slow Evolution System
Session Graph Model Perform Good
Fast Evolution System
Session Graph Model Perform Bad

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. 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)