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An Enhanced Semi-supervised Recommendation Model Based on Green’s Function

An Enhanced Semi-supervised Recommendation Model Based on Green’s Function. Dingyan Wang and Irwin King Dept. of Computer Science & Engineering The Chinese University of Hong Kong. Outline. Background Motivation An Enhanced Model Experimental Analysis Conclusion. Background.

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An Enhanced Semi-supervised Recommendation Model Based on Green’s Function

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  1. An Enhanced Semi-supervised Recommendation Model Based on Green’s Function Dingyan Wang and Irwin King Dept. of Computer Science & Engineering The Chinese University of Hong Kong ICONIP 2010, Sydney, Australia

  2. Outline • Background • Motivation • An Enhanced Model • Experimental Analysis • Conclusion ICONIP 2010, Sydney, Australia

  3. Background • Recommendation in Collaborative Filtering Recommendation ICONIP 2010, Sydney, Australia

  4. Background • Significance • Consumer Satisfaction • Profit • Mathematical Form • User-item matrix complete task • Rating prediction Rating for Prediction User Item ICONIP 2010, Sydney, Australia

  5. Background • Traditional Recommendation Methods • Memory-based method • Item-based method, WWW ’01 & SIGIR ’06 • User-based method, SIGIR ’06 • Model-based method • Probabilistic matrix factorization, SIGIR ’07 & 04 ICONIP 2010, Sydney, Australia

  6. Background • A Novel View of Recommendation [Green’s function recommendation, KDD ’07 & WWW10] • Label propagation on a graph • Label prediction with semi-supervised learning 2 1 3 4 5 ICONIP 2010, Sydney, Australia

  7. Motivation • Higher accuracy in label propagation recommendation • Importance of graph construction • Accuracy Reduction • Data Sparsity • Some items have no similarity information • Information Loss • Similarity in a local view ICONIP 2010, Sydney, Australia

  8. An Enhanced Model • An Enhanced Model Based on Green’s Function User-Item Rating Matrix Predicted User-item Matrix Enhanced Item-Graph Construction Green’s Function Calculation Label Propagation ICONIP 2010, Sydney, Australia

  9. An Enhanced Model • Enhanced Item-Graph Construction • Global similarity between items • Latent-feature vector similarity • Local similarity between items • Similarity derived from ratings • Global and local consistent similarity • Linear combination of global and local similarity ICONIP 2010, Sydney, Australia

  10. An Enhanced Model • Global Similarity Calculation • Latent features extraction • Probabilistic matrix factorization (PMF), NIPS ’08 : M*N rating matrix ; : K*N item-latent matrix : M*K user-latent : rating of user i for item j; : indicator to show whether user i rated item j. ICONIP 2010, Sydney, Australia

  11. An Enhanced Model • Local Similarity Calculation • Cosine Similarity • Pearson Correlation Coefficient (PCC) ICONIP 2010, Sydney, Australia

  12. An Enhanced Model • Global And Local Consistent Similarity (GLCS) • Global similarity from item latent matrix • Global and Local similarity combination • Weighted undirected item-graph ICONIP 2010, Sydney, Australia

  13. An Enhanced Model • Green’s Function Calculation (An Example) • Given an item-graph • Calculate the Laplacian matrix L= D-W W= 1 2 3 4 D= 5 ICONIP 2010, Sydney, Australia

  14. An Enhanced Model • Green’s Function Calculation • Defined as the inverse of matrix L with zero-mode discarded without ICONIP 2010, Sydney, Australia

  15. An Enhanced Model • Label Propagation Recommendation • rating as label ; • Closed form label propagation: Label Propagation Label data Unlabeled data ICONIP 2010, Sydney, Australia

  16. Experimental Analysis • Dataset • MovieLens dataset • Metrics • Mean Absolute Error (MAE) • Mean Zero-one Error (MZOE) • Rooted Mean Squared Error (RMSE) ICONIP 2010, Sydney, Australia

  17. Experimental Analysis • Impact of Weight Parameter k=5 k=10 ICONIP 2010, Sydney, Australia

  18. Experimental Analysis • Performance Comparison • Previous Green’s function model (GCOS, GPCC), [KDD ’07] • Item-based recommendation (ICOS, IPCC) • User-based recommendation (UCOS, UPCC) ICONIP 2010, Sydney, Australia

  19. Conclusion • Latent features provide global similarity. • Global and local consistent similarity can improve item-graph construction. • The enhanced model outperformed other memory-based methods and previous model. ICONIP 2010, Sydney, Australia

  20. Q&A Thank you! ICONIP 2010, Sydney, Australia

  21. PMF • Probabilistic Matrix Factorization • Define a conditional distribution over the observed ratings as: Gaussian Distribution ICONIP 2010, Sydney, Australia

  22. PMF • PMF • Assume zero-mean spherical Gaussian priors on user and item feature • By Bayesian Inference: ICONIP 2010, Sydney, Australia

  23. PMF • PMF • Optimization: to maximize the log likelihood of the posterior distribution: • Using Gradient Decent in Y, U, V to get local optimal. ICONIP 2010, Sydney, Australia

  24. Algorithm • Algorithm ICONIP 2010, Sydney, Australia

  25. ICONIP 2010, Sydney, Australia

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