{
  "id": "1810.02567",
  "version": "v1",
  "published": "2018-10-05T08:39:00.000Z",
  "updated": "2018-10-05T08:39:00.000Z",
  "title": "Online Learning to Rank with Features",
  "authors": [
    "Shuai Li",
    "Tor Lattimore",
    "Csaba Szepesvári"
  ],
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "We introduce a new model for online ranking in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter. Only relatively mild assumptions are made on the examination function. A novel algorithm for this setup is analysed, showing that the dependence on the number of items is replaced by a dependence on the dimension, allowing the new algorithm to handle a large number of items. When reduced to the orthogonal case, the regret of the algorithm improves on the state-of-the-art.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-10-05T08:39:00.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "online learning",
      "attractiveness function",
      "click probability factors",
      "orthogonal case",
      "linear function"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}