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      "statement": "# RankNet: pairwise probabilistic neural ranking\n\nTrained a scalar neural ranking function from document pairs using logistic score differences and cross-entropy loss, extending backpropagation to pairwise preferences while retaining individual-item scoring at inference.\n\n## Scientific scope\n\nPairwise loss does not directly optimize every listwise retrieval metric, nor guarantee global neural optimization. Does not invent gradient descent, pairwise preference learning or logistic models.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nChris Burges; Tal Shaked; Erin Renshaw; Ari Lazier; Matt Deeds; Nicole Hamilton; Greg Hullender\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Learning to Rank using Gradient Descent](https://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf)\n",
      "summary": "Trained a scalar neural ranking function from document pairs using logistic score differences and cross-entropy loss, extending backpropagation to pairwise preferences while retaining individual-item scoring at inference.",
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