Transfer learning in heterogeneous collaborative filtering domains

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Transfer learning in heterogeneous collaborative filtering domains

  1. 1. 2013/3/27Transfer learning inheterogeneous collaborativefiltering domainsAuthors/ Weike Pan and Qiang YangAffiliation/ Dept. of CSE, Hong Kong University of Science and TechnologySource/ Journal of Artificial Intelligence (2013)Presenter/ Allen Wu 1
  2. 2. Outline• Introduction• Heterogeneous collaborative filtering problems 2013/3/27• Transfer by collective factorization• Experimental results• Conclusion 2
  3. 3. Introduction• Data sparsity is a major challenge in collaborative filtering (CF). • Overfitting can easily happen for prediction. 2013/3/27• Some auxiliary data of the form “like” or “dislike” may be more easily obtained. • It’s more convenient for users to express preference.• How do we take advantage of auxiliary knowledge to alleviate the sparsity problem?• Most existing transfer learning methods in CF consider auxiliary data from several perspectives. • User-side transfer, item-side transfer, knowledge-transfer. 3
  4. 4. Probabilistic Matrix Factorization(NIPS’08)• 2013/3/27 4
  5. 5. Social Recommendation (CIKM’08)• 2013/3/27 5
  6. 6. Collective Matrix Factorization (KDD’08)• 2013/3/27 6
  7. 7. CodeBook Transfer (IJCAI’09)• 2013/3/27 7
  8. 8. Rating-matrix generative model (ICML’09)• RMGM is derived and extended from FMM generative model, which can be formulated as 2013/3/27 • The difference: • It learns (U, V) and (U3, V3) alternatively. • A soft indicator matrix is used. E.g., U [0, 1]n d. 8
  9. 9. Heterogeneous collaborative filteringproblems• • 2013/3/27 9
  10. 10. Challenges• 2013/3/27 10
  11. 11. Overview of solution• 2013/3/27 11
  12. 12. Model formulation• Assume a user u’s rating on an item i in the target data, rui, is generated from 2013/3/27 • user-specific latent feature vector Uu 1 d, where u=1,…,n. • item-specific latent feature vector Vi 1 d, where i=1,…,m. • some data-dependent effect denoted as B d d. 12
  13. 13. Model formulation (Cont.)• Likelihood:• Prior: 2013/3/27• Posterior Likelihood Prior (Bayesian inference) • Log(Posterior)= Log(Likelihood Prior) 13
  14. 14. Model formulation• 2013/3/27 14
  15. 15. Learning the TCF 2013/3/27 15
  16. 16. Learning U and V in CMTF• Theorem 1. Given B and V, we can obtain the user-specific latent matrix U in a closed form. 2013/3/27 16
  17. 17. Learning U and V in CSVD• 2013/3/27 17
  18. 18. Learning U and V in CSVD(Cont.) 2013/3/27 18
  19. 19. • 2013/3/2719
  20. 20. Algorithm of TCF 2013/3/27 20
  21. 21. Data sets• 2013/3/27 21
  22. 22. Evaluation metrics• Summary of Data sets 2013/3/27• Evaluation metrics 22
  23. 23. Baselines and parameter settings• 2013/3/27 23
  24. 24. Performance of Moviepilot data 2013/3/27 24
  25. 25. Performance of Netfliex data 2013/3/27 25
  26. 26. Performance on Netflix at differentsparsity levels• SCVD performs better than CMTF in 2013/3/27 all cases. 26
  27. 27. Conclusion• This paper investigate how to address the sparsity problem in CF via a transfer learning solution. 2013/3/27• The TCP framework is proposed to transfer knowledge from auxiliary data to target data to alleviates the data sparsity.• Experimental results show that TCP performs significantly better than several state-of-the-art baseline algorithms.• In the future, the “pure” cold-start problem for users without any rating is needed to be addressed via transfer learning. 27
  28. 28. 2013/3/27Thank you forlistening.Q&A 28

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