{
  "id": "1804.05090",
  "version": "v1",
  "published": "2018-04-13T18:54:30.000Z",
  "updated": "2018-04-13T18:54:30.000Z",
  "title": "Regularized Singular Value Decomposition and Application to Recommender System",
  "authors": [
    "Shuai Zheng",
    "Chris Ding",
    "Feiping Nie"
  ],
  "categories": [
    "cs.LG",
    "cs.IR",
    "stat.ML"
  ],
  "abstract": "Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA). Together, SVD and PCA are one of the most widely used mathematical formalism/decomposition in machine learning, data mining, pattern recognition, artificial intelligence, computer vision, signal processing, etc. In recent applications, regularization becomes an increasing trend. In this paper, we present a regularized SVD (RSVD), present an efficient computational algorithm, and provide several theoretical analysis. We show that although RSVD is non-convex, it has a closed-form global optimal solution. Finally, we apply RSVD to the application of recommender system and experimental result show that RSVD outperforms SVD significantly.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-04-13T18:54:30.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "regularized singular value decomposition",
      "recommender system",
      "application",
      "closed-form global optimal solution",
      "principal component analysis"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}