Abstract
Collaborative filtering usually suffers from limited performance due to a data sparsity problem. Transfer learning presents an unprecedented opportunity to alleviate this issue through the transfer of useful knowledge from an auxiliary domain to a target domain. However, the situation becomes complicated when the source and target domain share partial knowledge with each other. Transferring the unshared part across domains will cause negative transfer and may degrade the prediction accuracy in the target domain. To address this issue, in this paper, we present a novel model that exploits the latent factors in the target domain against the negative transfer. First, we transfer rating patterns from the source domain to approximate and reconstruct the target rating matrix. Second, to be specific, we propose a partial-adaptation nonnegative matrix factorization method to correct the transfer learning result and restore latent factors in the target. The final experiments completed on real world datasets demonstrate that our proposed approach effectively addresses the negative transfer and significantly outperforms the state-of-art transfer-learning model.
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The work is supported by the Beijing Natural Science Foundation (No. 4192008) and the General Project of Beijing Municipal Education Commission (No. KM201710005023).
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He, M., Zhang, J. & Zhang, J. Restoring latent factors against negative transfer using partial-adaptation nonnegative matrix factorization. CCF Trans. Pervasive Comp. Interact. 2, 42–50 (2020). https://doi.org/10.1007/s42486-019-00018-x
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DOI: https://doi.org/10.1007/s42486-019-00018-x