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
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
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
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