Adapting ranking SVM to document retrieval

By Yunbo Cao, Jun Xu, Tie-Yan Liu, Hang Li, Yalou Huang, Hsiao-Wuen Hon. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006

597 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/7f0fc30f88279f7ba8be3a5ea00b0753df8f5afc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

Multimodal Fusion for Video Search Reranking (2010, methodology): “Before presenting the proposed reranking scheme, in this section, we first analyze the weakness in those search engines and then judge whether it is possible to alleviate the weakness using the reranking technique.”
Early exit optimizations for additive machine learned ranking systems (2010, methodology): “Pairwise learning is now one of the most popular techniques: it can be used directly with linear functions [9] or it can be combined within the boosted decision tree framework [33].”
LETOR: Benchmark Dataset for Research on Learning to Rank for Information Retrieval (2007, methodology): “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]).”
LETOR: A benchmark collection for research on learning to rank for information retrieval (2010, methodology): “These two corpora were selected because (1) they were publicly available (Voorhees et al. 2005) and (2) they had been widely used by previous work on ranking in the literature of IR (Cao et al. 2006; Craswell et al. 2004, 2003; Robertson et al. 2000).”
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].”
"On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking" (2012, background): “Most algorithms for learning to rank fall into the latter category, including the reference algorithms RankBoost [12] and Ranking SVMs [14, 4] or the regression approach of [8], because convex surrogate losses lead to optimization problems that can be solved efﬁciently while non-convex approaches…”
Deep semantic ranking based hashing for multi-label image retrieval (2015, methodology): “Inspired by [1], we modify the ranking loss by adding adaptive weights related to the similarity levels of database points:”
Ranking with ordered weighted pairwise classification (2009, methodology): “The idea of weighting losses has already been used in (Cao et al., 2006) and (Cossock & Zhang, 2006).”
Ranking and empirical minimization of U-statistics (2006, background): “…problem has received increasing attention both in the statistical and machine learning literature (see, e.g., Agarwal et al. [2], Cao et al. [11], Cortes and Mohri [12], Cossock and Zhang [13], Freund, Iyer, Schapire and Singer [17], Rudin [35], Usunier et al. [44] and Vittaut and Gallinari…”
A Short Introduction to Learning to Rank (2011, unknown): “The pairwise approach includes Ranking SVM [7], RankBoost [8], RankNet [9], GBRank [14], IR SVM [15], Lambda Rank [16], and LambdaMART [17].”
Multiple-instance ranking: Learning to rank images for image retrieval (2008, methodology): “Similar to some previous work in rank learning [5, 15, 17] and MI learning [2, 9], we formulate the problem in the large margin framework.”
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].”