1. 2013/3/27
Transfer learning in
heterogeneous collaborative
filtering domains
Authors/ Weike Pan and Qiang Yang
Affiliation/ Dept. of CSE, Hong Kong University of Science and Technology
Source/ Journal of Artificial Intelligence (2013)
Presenter/ Allen Wu
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2. Outline
• Introduction
• Heterogeneous collaborative filtering problems
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• Transfer by collective factorization
• Experimental results
• Conclusion
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3. Introduction
• Data sparsity is a major challenge in collaborative filtering (CF).
• Overfitting can easily happen for prediction.
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• 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. Probabilistic Matrix Factorization
(NIPS’08)
•
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5. Social Recommendation (CIKM’08)
•
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6. Collective Matrix Factorization (KDD’08)
•
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7. CodeBook Transfer (IJCAI’09)
•
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8. Rating-matrix generative model (ICML’09)
• RMGM is derived and extended from FMM generative model,
which can be formulated as
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• The difference:
• It learns (U, V) and (U3, V3) alternatively.
• A soft indicator matrix is used. E.g., U [0, 1]n d.
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9. Heterogeneous collaborative filtering
problems
• •
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10. Challenges
•
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11. Overview of solution
•
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12. Model formulation
• Assume a user u’s rating on an item i in the target data, rui, is
generated from
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• 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.
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13. Model formulation (Cont.)
• Likelihood:
• Prior:
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• Posterior Likelihood Prior (Bayesian inference)
• Log(Posterior)= Log(Likelihood Prior)
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14. Model formulation
•
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15. Learning the TCF
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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.
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17. Learning U and V in CSVD
•
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18. Learning U and V in CSVD
(Cont.)
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19. •
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20. Algorithm of TCF
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21. Data sets
•
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22. Evaluation metrics
• Summary of Data sets
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• Evaluation metrics
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23. Baselines and parameter settings
•
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24. Performance of Moviepilot data
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25. Performance of Netfliex data
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26. Performance on Netflix at different
sparsity levels
• SCVD performs
better than CMTF in
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all cases.
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27. Conclusion
• This paper investigate how to address the sparsity problem in
CF via a transfer learning solution.
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• 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.
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28. 2013/3/27
Thank you for
listening.
Q&A
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