This document presents a method for transferring knowledge between heterogeneous collaborative filtering domains to address data sparsity problems. The authors propose a transfer collective factorization (TCF) framework that learns shared latent features from auxiliary "like" data and applies them to predict ratings in a target domain. TCF extends probabilistic matrix factorization and collective matrix factorization models. Experimental results on Movielens and Netflix datasets show TCF improves rating predictions over baseline methods, especially at high sparsity levels. The authors conclude TCF is an effective transfer learning approach for collaborative filtering but note future work could focus on addressing the cold start problem for users without any ratings.

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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