212 / 2020-01-01 21:27:00
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion
全文录用
Kaiyi Ji / The Ohio State University, USA
Jian Tan / Alibaba Group & The Ohio State University, USA
Jinfeng Xu / The University of Hong Kong, Hong Kong
Yuejie Chi / Carnegie Mellon University, USA
Low-rank matrix completion has achieved great success in many real-world data applications. A matrix factorization model that learns latent features is usually employed and, to improve prediction performance, the similarities between latent variables can be exploited by pairwise learning using the graph regularized matrix factorization (GRMF) method. However, existing GRMF approaches often use the squared loss to measure the pairwise differences, which may be overly influenced by dissimilar pairs and lead to inferior prediction. To fully empower pairwise learning for matrix completion, we propose a general optimization framework that allows a rich class of (non-)convex pairwise penalty functions. A new and efficient algorithm
is developed to solve the proposed optimization problem. We conduct extensive experiments on real recommender datasets to
重要日期
  • 会议日期

    06月08日

    2020

    06月11日

    2020

  • 01月12日 2020

    初稿截稿日期

  • 04月15日 2020

    提前注册日期

  • 12月31日 2020

    注册截止日期

主办单位
IEEE Signal Processing Society
承办单位
Zhejiang University
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