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Graph-based Recommendation on Social Networks (IEEE2010 International Asia-Pacific Web Conference)

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

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Graph-based Recommendation on Social Networks (IEEE2010 International Asia-Pacific Web Conference)

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  1. 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

  2. Recommender Algorithm • 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.

  3. Random Walk with Restarts(RWR) How closely related are two nodes in a graph？ a = in every step there is a probability q = is a column vector of zeros with the element corresponding to the starting node set to 1 S = is the transition probability matrix and its element P(t) = denotes the probability that the random walk at step t

  4. 10 9 12 2 8 1 11 3 4 6 5 7 Random Walk with Restarts(RWR)

  5. 0.03 0.04 10 9 0.10 12 2 0.08 0.02 0.13 8 1 0.13 11 3 0.04 4 0.05 6 5 0.13 7 0.05 Random Walk with Restarts(RWR) Nearby nodes, higher scores Ranking vector More red, more relevant

  6. Tag-based Promotion Algorithms

  7. 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.

  8. Tag-based Promotion Algorithms

  9. Tag-based Promotion Algorithms to describe the interest of user ti(k) = is the kth tag made by user ui. ci(k) = the frequency of tag ti(k)

  10. Tag-based Promotion Algorithms • Measuring the user’s similarity based on their tagging information. ni = is the number of tags user ui assigned. ci(k) = the frequency of tag ti(k)

  11. Tag-based Promotion Algorithms • The weight of the edge should be proportional to the similarity. k = is a parameter that we will test it in the experiment.

  12. Evaluation Protocol TS = stands for test set U = stands for users set RelevantNum = the number of relevant resources in the results RecommendLength = the number of resources that are recommended to a user

  13. Two information retrieval metrics • 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.

  14. Experiment Method 1 = Assigning the minimum value Method 2 = Assigning the maximum value Method 3 = Assigning the average rating

  15. Experiment

  16. Experiment

  17. Conclusions and Future work • 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.

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