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  2. Learning to rank - Wikipedia

    en.wikipedia.org/wiki/Learning_to_rank

    Learning to rank [1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. [2] Training data may, for example, consist of lists of items with some partial order specified between items in ...

  3. EdgeRank - Wikipedia

    en.wikipedia.org/wiki/EdgeRank

    EdgeRank is the name commonly given to the algorithm that Facebook uses to determine what articles should be displayed in a user's News Feed.As of 2011, Facebook has stopped using the EdgeRank system and uses a machine learning algorithm that, as of 2013, takes more than 100,000 factors into account.

  4. Ranking SVM - Wikipedia

    en.wikipedia.org/wiki/Ranking_SVM

    In machine learning, a ranking SVM is a variant of the support vector machine algorithm, which is used to solve certain ranking problems (via learning to rank). The ranking SVM algorithm was published by Thorsten Joachims in 2002. [1] The original purpose of the algorithm was to improve the performance of an internet search engine. However, it ...

  5. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [494] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...

  6. RankBrain - Wikipedia

    en.wikipedia.org/wiki/RankBrain

    RankBrain is a machine learning-based search engine algorithm, the use of which was confirmed by Google on 26 October 2015. [1] It helps Google to process search results and provide more relevant search results for users. [2]

  7. Preference learning - Wikipedia

    en.wikipedia.org/wiki/Preference_learning

    Preference learning is a subfield of machine learning that focuses on modeling and predicting preferences based on observed preference information. [1] Preference learning typically involves supervised learning using datasets of pairwise preference comparisons, rankings, or other preference information.

  8. LightGBM - Wikipedia

    en.wikipedia.org/wiki/LightGBM

    LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [4] [5] It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and ...

  9. AAAI Conference on Artificial Intelligence - Wikipedia

    en.wikipedia.org/wiki/AAAI_Conference_on...

    The AAAI Conference on Artificial Intelligence (AAAI) is a leading international academic conference in artificial intelligence held annually. [1] [2] [3] It ranks 4th in terms of H5 Index in Google Scholar's list of top AI publications, after ICLR, NeurIPS, and ICML. [4]

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