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