graph-based recommendation on social networks (ieee2010 international asia-pacific web conference)
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Graph-based Recommendation on Social Networks (IEEE2010 International Asia-Pacific Web Conference). School of Electronics Engineering and Computer Science Peking University Beijing, P.R. China Ziqi Wang, Yuwei Tan, Ming Zhang. Recommender Algorithm. Content-based recommendation - PowerPoint PPT PresentationTRANSCRIPT
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?
Random Walk with Restarts(RWR)
1
4
3
2
56
7
910
8
11
12
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
Tag-based Promotion Algorithms
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
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
Experiment
Experiment
Conclusions◦ Two algorithms based on the framework of
Random Walk with Restarts.◦ This proves that our promotion algorithm
performs better on sparse data sets. Future work
◦ Focus on recommendation on large scale data with better performance and lower time cost.
Conclusions and Future work