AdaRank: a boosting algorithm for information retrieval

By Jun Xu, Hang Li. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2007

936 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/a489d95fb930401c1f4b7d92bb139d271d49abbf

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Citations in the corpus

Ranking with ordered weighted pairwise classification (2009, methodology): “Considering the example of document retrieval, when given a query, the scoring function associates a scalar to each document (the score).”
LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval (2007, background): “Many methods have been proposed (e.g., [2], [5], [6], [7], [10], [16], [20], [22], [23]) and applied to IR applications (e.g., [3], [12], [27], [31], [32], [33]).”
Seq2Slate: Re-ranking and Slate Optimization with RNNs (2018, methodology): “Using the generated training data, we train both our seq2slate model and baseline rankers from the RankLib package: AdaRank [Xu and Li, 2007], Coordinate Ascent [Metzler and Croft, 2007], LambdaMART [Wu et al., 2010], ListNet [Cao et al., 2007], MART [Friedman, 2001], Random Forests [Breiman,…”
LETOR: A benchmark collection for research on learning to rank for information retrieval (2010, methodology): “A query set with 106 queries on the OHSUMED corpus has been widely used in previous work (Qin et al. 2007; Xu et al. 2007), in which each query describes a medical search need (associated with patient information and topic information).”
Metric Learning to Rank (2010, background): “Viewed in this light, we can cast nearest neighbor prediction as a ranking problem, and the predicted label error rate as a loss function over rankings.”
An apple-to-apple comparison of Learning-to-rank algorithms in terms of Normalized Discounted Cumulative Gain (2012, methodology): “ADARANK [9] is a listwise boosting approach aiming to optimize an arbitrary listwise IR metrics, such as the Mean Average Precision (MAP), ERR, or NDCG.”
A cascade ranking model for efficient ranked retrieval (2011, methodology): “We propose a novel boosting algorithm, a generalization of AdaRank [30], that jointly learns the cascade structure and parameters.”
Integer optimization methods for machine learning (2012, methodology): “…supervised ranking methods are used almost exclusively for large scale problems that occur in the information retrieval domain [for example, see Xu, 2007, Cao et al., 2007, Matveeva et al., 2006, Lafferty and Zhai, 2001, Li et al., 2007, and the LETOR compilation of works 2 ], and there are…”
Learning to Rank Using User Clicks and Visual Features for Image Retrieval (2015, methodology): “In these experiments, we test the performance of these methods by varying the depth p of NDCG in the range [3, 5, 10, 25, 50].”
TweetCred: Real-Time Credibility Assessment of Content on Twitter (2014, methodology): “We experimented with various methods that are typically used for information retrieval tasks: Coordinate Ascent [13], AdaRank [20], RankBoost [5] and SVM-rank [11].”
Adapting boosting for information retrieval measures (2010, neutral): “Other approaches have modified AdaBoost for NDCG [21] and have considered ranking using the whole list of returned results as input for computing the score of a given document [6].”
Reciprocal rank fusion outperforms condorcet and individual rank learning methods (2009, neutral): “AdaRank-MAP [ 6 ] 0.5778 (0.55 - 0.61) 0.0273 (0.01 - 0.04) .000”
Direct Learning to Rank and Rerank (2018, methodology): “, airplanes, as considered by Oza et al, 2009), it could be useful for prioritizing maintenance on the power grid (Rudin et al, 2012, 2010), it could be used for ranking medical workers in order of likelihood that they accessed medical records inappropriately (as considered by Menon et al, 2013), pr”
entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation (2017, methodology): “We experiment two listwise algorithms, AdaRank [27] and LambdaMart [4], on val-”
A general approximation framework for direct optimization of information retrieval measures (2010, methodology): “AdaRank (Xu and Li 2007) minimizes an exponential loss function which can upper bound either (1 - AP) or (1 - NDCG) using boosting methods.”