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Transcript of School of Electronics Engineering and Computer Science Peking University Beijing, P.R. China Ziqi...
Graph-based Recommendation on Social Networks
(IEEE2010 International Asia-Pacific Web Conference)
School of Electronics Engineering and Computer Science Peking UniversityBeijing, P.R. China
Ziqi Wang, Yuwei Tan, Ming Zhang
Content-based recommendation◦ Recommends resources based on their content
and not on user’s rating and opinion. Collaborative filtering
◦ It’s based on the assumption that similar users express similar interests on similar resources.
Graph based recommendation◦ User transitive associations between users and
resources in the bipartite user-resource graph.
Recommender Algorithm
Random Walk with Restarts(RWR)
aqSpap tt )()1( )1(
a = in every step there is a probabilityq = is a column vector of zeros with the element corresponding to the starting node set to 1S = is the transition probability matrix and its elementP(t) = denotes the probability that the random walk at step t
How closely related are two nodes in a graph?
Node 4
Node 1Node 2Node 3Node 4Node 5Node 6Node 7Node 8Node 9Node 10Node 11Node 12
0.130.100.130.220.130.050.050.080.040.030.040.02
Random Walk with Restarts(RWR)
1
4
3
2
56
7
910
811
120.13
0.10
0.13
0.13
0.05
0.05
0.08
0.04
0.02
0.04
0.03
Ranking vector More red, more relevant
Nearby nodes, higher scores
4r
Treating tagging behavior directly as another form of rating.◦ Assigning the minimum value of user rating to be
the weight of each new edge◦ Assigning the maximum value of user rating to
be the weight of each new edge◦ Assigning the average rating of the
corresponding user to be the weight of the new edge
Choose the best one in the experiment.
Tag-based Promotion Algorithms
Tag-based Promotion Algorithms
ti(k) = is the kth tag made by user ui.
ci(k) = the frequency of tag ti
(k)
ii ct , to describe the interest of user
Measuring the user’s similarity based on their tagging information.
Tag-based Promotion Algorithms
jiji uuuusim ,cos,
jin
k
kj
n
k
ki
Tt
tj
ti
ji
ji
cc
cc
uu
uu
11
22||||
ni = is the number of tags user ui assigned.ci
(k) = the frequency of tag ti(k)
The weight of the edge should be proportional to the similarity.
Tag-based Promotion Algorithms
ji uusimkw ,*
k = is a parameter that we will test it in the experiment.
Evaluation Protocol
gthcommendLenU
levantNumprecision
TS
levantNumrecall
Re*||
Re
||
Re
TS = stands for test setU = stands for users setRelevantNum = the number of relevant resources in the resultsRecommendLength = the number of resources that are recommended to a user
P@k = Precision at rank K◦ The proportion of resources ranked in the top K
results. S@k = Success at rank K
◦ The probability of finding a good resource among the top K results.
Two information retrieval metrics
Experiment
Method 1 = Assigning the minimum valueMethod 2 = Assigning the maximum valueMethod 3 = Assigning the average rating