Improving maximum margin matrix factorization

被引：0
|
作者：
Markus Weimer
Alexandros Karatzoglou
Alex Smola
机构：
[1] Technische Universität Darmstadt,
[2] INSA de Rouen,undefined
[3] LITIS,undefined
[4] NICTA,undefined
来源：
Machine Learning | 2008年 / 72卷
关键词：
Collaborative filtering; Structured estimation; Recommender systems;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful learning approach to this task and has been recently extended to structured ranking losses. In this paper we discuss a number of extensions to MMMF by introducing offset terms, item dependent regularization and a graph kernel on the recommender graph. We show equivalence between graph kernels and the recent MMMF extensions by Mnih and Salakhutdinov (Advances in Neural Information Processing Systems 20, 2008). Experimental evaluation of the introduced extensions show improved performance over the original MMMF formulation.
引用
收藏
页码：263 / 276
页数：13
相关论文
共 50 条
  • [1] Improving maximum margin matrix factorization
    Weimer, Markus
    Karatzoglou, Alexandros
    Smola, Alex
    [J]. MACHINE LEARNING, 2008, 72 (03) : 263 - 276
  • [2] Proximal maximum margin matrix factorization for collaborative filtering
    Kumar, Vikas
    Pujari, Arun K.
    Sahu, Sandeep Kumar
    Kagita, Venkateswara Rao
    Padmanabhan, Vineet
    [J]. PATTERN RECOGNITION LETTERS, 2017, 86 : 62 - 67
  • [3] Combining Swarm with Gradient Search for Maximum Margin Matrix Factorization
    Salman, K. H.
    Pujari, Arun K.
    Kumar, Vikas
    Veeramachaneni, Sowmini Devi
    [J]. PRICAI 2016: TRENDS IN ARTIFICIAL INTELLIGENCE, 2016, 9810 : 167 - 179
  • [4] Scalable Maximum Margin Matrix Factorization by Active Riemannian Subspace Search
    Yan, Yan
    Tan, Mingkui
    Tsang, Ivor
    Yang, Yi
    Zhang, Chengqi
    Shi, Qinfeng
    [J]. PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI), 2015, : 3988 - 3994
  • [5] Multiplicative Update Rules for Concurrent Nonnegative Matrix Factorization and Maximum Margin Classification
    Zoidi, Olga
    Tefas, Anastasios
    Pitas, Ioannis
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2013, 24 (03) : 422 - 434
  • [6] Non-negative Matrix Factorization as a Feature Selection Tool for Maximum Margin Classifiers
    Das Gupta, Mithun
    Xiao, Jing
    [J]. 2011 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2011,
  • [7] A Maximum Margin Matrix Factorization based Transfer Learning Approach for Cross-Domain Recommendation
    Veeramachaneni, Sowmini Devi
    Pujari, Arun K.
    Padmanabhan, Vineet
    Kumar, Vikas
    [J]. APPLIED SOFT COMPUTING, 2019, 85
  • [8] Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization
    Shaikh, Shamal
    Kagita, Venkateswara Rao
    Kumar, Vikas
    Pujari, Arun K.
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2024, 238
  • [9] Thermal to Visual Person Re-Identification Using Collaborative Metric Learning Based on Maximum Margin Matrix Factorization
    Gavini, Yaswanth
    Agarwal, Arun
    Mehtre, B. M.
    [J]. PATTERN RECOGNITION, 2023, 134
  • [10] Generating Pseudotransactions for Improving Sparse Matrix Factorization
    Wibowo, Agung T.
    [J]. PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), 2016, : 439 - 442
12345