McRank: Learning to Rank Using Multiple Classification and Gradient Boosting

By Ping Li, C. Burges, Qiang Wu. 2007

585 citations recorded by the source corpus.

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Citations in the corpus

LETOR: A benchmark collection for research on learning to rank for information retrieval (2010, methodology): “According to different output spaces of the ranking function, the pointwise approach can be further categorized as regression based algorithms (Li et al. 2008), classification based algorithms (Li et al.”
Learning to rank for information retrieval (2009, unknown): “In recent years, learning to rank has become a very hot research direction in IR, and a large number of learning-to-rank algorithms have been proposed, such as [9, 13, 14, 16, 17, 26, 29, 33, 34, 47, 49, 59, 63, 73, 78, 90, 97, 99, 102, 114, 119, 122, 129, 134, 136].”
The BellKor Solution to the Netflix Grand Prize (2009, background): “This has made them a good choice for severa l large scale practical problems such as ranking results of a search engine [9, 17], or query-biased summarization of sea rch results [10].”
A Survey of Techniques for Event Detection in Twitter (2015, background): “Therefore, several learning to rank models have been proposed for information retrieval tasks to capture document relevance by combining various global, context-specific, and user-specific features (Li et al. 2008).”
Ranking Measures and Loss Functions in Learning to Rank (2009, methodology): “Thepointwise approach, such as subset regression [5] and McRank [10], views each single object as the learning instance.”
Learning to Rank Lexical Substitutions (2013, background): “To predict a ranking of a set of query instances, these are simply sorted by their predicted scores (Li et al., 2007).”
A Short Introduction to Learning to Rank (2011, methodology): “The pointwise approach includes Subset Ranking [6], McRank [11], Prank [12], and OC SVM [13].”
An Introduction to Neural Information Retrieval (2018, unknown): “The cross-entropy loss can then be applied [Li et al., 2008] as follows,”
Direct Learning to Rank and Rerank (2018, background): “…ranking water pipes in order of vulnerability (as considered by Li et al, 2013), other areas of information retrieval (Xu, 2007; Cao et al, 2007; Matveeva et al, 2006; Lafferty and Zhai, 2001; Li et al, 2007) and in almost any domain where one measures the quality of results by rank statistics.”
Ordinal hyperplanes ranker with cost sensitivities for age estimation (2011, background): “Some early schemes simply performed ranking based on regression [6] or classification [16].”
Integer optimization methods for machine learning (2012, background): “…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 many works that discuss how to approximately solve extremely large ranking…”
Adapting boosting for information retrieval measures (2010, unknown): “1 of Li et al. 2007); our approach overcomes this drawback.”
How to reverse-engineer quality rankings (2012, background): “…ranking considers problems that are specific to information retrieval (e.g., Cao et al. 2007; Matveeva et al. 2006; Lafferty and Zhai 2001; Li et al. 2007) or give insight into how to approximately solve versions of extremely large ranking problems quickly (Tsochantaridis et al. 2005;…”
Cross-Domain Sentence Modeling for Relevance Transfer with BERT (2019, methodology): “[38] Pointwise algorithms such as McRank [33] approximate the LTR problem as a regression problem where a relevance score is predicted for each query-document pair.”
LightGBM: A Highly Efficient Gradient Boosting Decision Tree (2017, methodology): “Another popular algorithm is the histogram-based algorithm [10, 11, 12], as shown in Alg.”