From RankNet to LambdaRank to LambdaMART: An Overview

By C. Burges. 2010

1,431 citations recorded by the source corpus.

Subfields: Information Retrieval & Search

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Citations in the corpus

Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “roaches to text ranking using transformers. Learning to rank reached its zenith in the early 2010s, on the eve of the deep learning revolution, with the development of models based on tree ensembles [Burges, 2010].23 At that time, there was an emerging consensus that gradient-boosted decision trees ”
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (2017, neutral): “rrectly the items that the user will like at the top of the list (first 5, 10 or 20 positions). To achieve this with machine learning one has to typically utilize learning to rank techniques(see e.g. [3]) and in particular ranking objectives and loss functions. The current session-based RNN approach”
Traveling Salesman Problem (2008, neutral): “Specifically, the lambdaMART [9] algorithm, a boosted tree version of LambdaRank seems to perform significantly better on web search related test data.”
Evaluation Measures Based on Preference Graphs (2021, methodology): “Evaluation measures such as nDCG [4, 19] and ERR [9] then employ these grades to compute effectiveness scores that are intended to provide an indication of a ranker’s performance.”
An anatomy for neural search engines (2019, methodology): “Perhaps the most successful examples of classical L2R methods are RankNet, LambdaRank, and LambdaMART [Burges, 2010].”
Label Partitioning For Sublinear Ranking (2013, background): “Finally, in information retrieval where one is required to rank a set of documents, SVMs (Yue et al., 2007; Grangier & Bengio, 2008) and neural networks like LambdaRank and RankNet (Burges, 2010) are popular choices.”
Open Domain Question Answering via Semantic Enrichment (2015, methodology): “In our experiments, we use an inhouse fast implementation of the MART gradient boosting decision tree algorithm [9, 21] to learn our ranker using the training set of our data.”
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models (2017, methodology): “iscriminator are trained from scratch. In the experiments, we compare the generative retrieval model in our IRGAN framework with simple RankNet [3], LambdaRank [5], and the strong baseline LambdaMART [4] for which we use the RankLib5 implementation. For the evaluation of those compared algorithms, w”
Leveraging Passage-level Cumulative Gain for Document Ranking (2020, background): “For many existing ranking methods, no matter whether they are unsupervised (e.g. BM25 [34] and language models [32, 42]) or supervised (e.g. learning to rank [1, 22] or deep ranking [27]), they usually capture relevance signals at the whole document level.”
Learning to Rank (2005, neutral): “• Non-linear neural network and tree ensemble-based ranking models [3], exploration strategies based on uncertainty estimations [31].”
Bagging gradient-boosted trees for high precision, low variance ranking models (2011, background): “Readers can refer to [6] for details of this algorithm.”
Cross-Domain Sentence Modeling for Relevance Transfer with BERT (2019, methodology): “On the other hand, LTR is framed as a classification problem by pairwise algorithms like RankSVM [27] and LambdaMART [12] with the goal of selecting the more relevant one given a pair of documents.”
Position Bias Estimation for Unbiased Learning to Rank in Personal Search (2018, methodology): “In addition, both Rank and Rank can be optimized using the λ-gradient in LambdaRank or LambdaMART [4].”
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion (2023, background): “, 2005) and LambdaRank (Burges, 2010) have emerged as powerful techniques for various ranking problems.”
entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation (2017, methodology): “LambdaMart is the combination of LambdaRank and the boosted tree method MART [10]. e key idea is to optimize the loss function by directly modeling its gradients estimates with respect to the model scores.”