Transfer learning in

heterogeneous collaborative

filtering domainsAuthors/ 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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Outline

• Introduction

• Heterogeneous collaborative filtering problems

• Transfer by collective factorization

• Experimental results

• Conclusion

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Introduction

• Data sparsity is a major challenge in collaborative filtering (CF).

• Overfitting can easily happen for prediction.

• 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.

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Probabilistic Matrix Factorization

(NIPS’08)

•

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Social Recommendation (CIKM’08)

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Collective Matrix Factorization (KDD’08)

•

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CodeBook Transfer (IJCAI’09)

•

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Rating-matrix generative model (ICML’09)

• RMGM is derived and extended from FMM generative model,

which can be formulated as

• 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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Heterogeneous collaborative filtering

problems

• •

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Challenges

•

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Overview of solution

•

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Model formulation

• Assume a user u’s rating on an item i in the target data, rui, is

generated from

• user-specific latent feature vector Uu1 d, where u=1,…,n.

• item-specific latent feature vector Vi1 d, where i=1,…,m.

• some data-dependent effect denoted as B d d.

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Model formulation (Cont.)

• Likelihood:

• Prior:

• Posterior Likelihood Prior (Bayesian inference)

• Log(Posterior)= Log(Likelihood Prior)

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Model formulation

•

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Learning the TCF

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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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Learning U and V in CSVD

•

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Learning U and V in CSVD

(Cont.)

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•

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Algorithm of TCF

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Data sets

•

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Evaluation metrics

• Summary of Data sets

• Evaluation metrics

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Baselines and parameter settings

•

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Performance of Moviepilot data

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Performance of Netfliex data

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Performance on Netflix at different

sparsity levels

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• SCVD performs

better than CMTF in

all cases.

Conclusion

• This paper investigate how to address the sparsity problem in

CF via a transfer learning solution.

• 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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Thank you for listening.Q & A

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