Learning to rank

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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 each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. "relevant" or "not relevant") for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data.

Contents

Applications

In information retrieval

A possible architecture of a machine-learned search engine. MLR-search-engine-example.svg
A possible architecture of a machine-learned search engine.

Ranking is a central part of many information retrieval problems, such as document retrieval, collaborative filtering, sentiment analysis, and online advertising.

A possible architecture of a machine-learned search engine is shown in the accompanying figure.

Training data consists of queries and documents matching them together with the relevance degree of each match. It may be prepared manually by human assessors (or raters, as Google calls them), who check results for some queries and determine relevance of each result. It is not feasible to check the relevance of all documents, and so typically a technique called pooling is used — only the top few documents, retrieved by some existing ranking models are checked. This technique may introduce selection bias. Alternatively, training data may be derived automatically by analyzing clickthrough logs (i.e. search results which got clicks from users), [3] query chains, [4] or such search engines' features as Google's (since-replaced) SearchWiki. Clickthrough logs can be biased by the tendency of users to click on the top search results on the assumption that they are already well-ranked.

Training data is used by a learning algorithm to produce a ranking model which computes the relevance of documents for actual queries.

Typically, users expect a search query to complete in a short time (such as a few hundred milliseconds for web search), which makes it impossible to evaluate a complex ranking model on each document in the corpus, and so a two-phase scheme is used. [5] First, a small number of potentially relevant documents are identified using simpler retrieval models which permit fast query evaluation, such as the vector space model, boolean model, weighted AND, [6] or BM25. This phase is called top- document retrieval and many heuristics were proposed in the literature to accelerate it, such as using a document's static quality score and tiered indexes. [7] In the second phase, a more accurate but computationally expensive machine-learned model is used to re-rank these documents.

In other areas

Learning to rank algorithms have been applied in areas other than information retrieval:

Feature vectors

For the convenience of MLR algorithms, query-document pairs are usually represented by numerical vectors, which are called feature vectors . Such an approach is sometimes called bag of features and is analogous to the bag of words model and vector space model used in information retrieval for representation of documents.

Components of such vectors are called features , factors or ranking signals. They may be divided into three groups (features from document retrieval are shown as examples):

Some examples of features, which were used in the well-known LETOR dataset:

Selecting and designing good features is an important area in machine learning, which is called feature engineering.

Evaluation measures

There are several measures (metrics) which are commonly used to judge how well an algorithm is doing on training data and to compare the performance of different MLR algorithms. Often a learning-to-rank problem is reformulated as an optimization problem with respect to one of these metrics.

Examples of ranking quality measures:

DCG and its normalized variant NDCG are usually preferred in academic research when multiple levels of relevance are used. [11] Other metrics such as MAP, MRR and precision, are defined only for binary judgments.

Recently, there have been proposed several new evaluation metrics which claim to model user's satisfaction with search results better than the DCG metric:

Both of these metrics are based on the assumption that the user is more likely to stop looking at search results after examining a more relevant document, than after a less relevant document.

Approaches

Tie-Yan Liu of Microsoft Research Asia has analyzed existing algorithms for learning to rank problems in his book Learning to Rank for Information Retrieval. [1] He categorized them into three groups by their input spaces, output spaces, hypothesis spaces (the core function of the model) and loss functions: the pointwise, pairwise, and listwise approach. In practice, listwise approaches often outperform pairwise approaches and pointwise approaches. This statement was further supported by a large scale experiment on the performance of different learning-to-rank methods on a large collection of benchmark data sets. [14]

In this section, without further notice, denotes an object to be evaluated, for example, a document or an image, denotes a single-value hypothesis, denotes a bi-variate or multi-variate function and denotes the loss function.

Pointwise approach

In this case, it is assumed that each query-document pair in the training data has a numerical or ordinal score. Then the learning-to-rank problem can be approximated by a regression problem — given a single query-document pair, predict its score. Formally speaking, the pointwise approach aims at learning a function predicting the real-value or ordinal score of a document using the loss function .

A number of existing supervised machine learning algorithms can be readily used for this purpose. Ordinal regression and classification algorithms can also be used in pointwise approach when they are used to predict the score of a single query-document pair, and it takes a small, finite number of values.

Pairwise approach

In this case, the learning-to-rank problem is approximated by a classification problem — learning a binary classifier that can tell which document is better in a given pair of documents. The classifier shall take two documents as its input and the goal is to minimize a loss function . The loss function typically reflects the number and magnitude of inversions in the induced ranking.

In many cases, the binary classifier is implemented with a scoring function . As an example, RankNet [15] adapts a probability model and defines as the estimated probability of the document has higher quality than :

where is a cumulative distribution function, for example, the standard logistic CDF, i.e.

Listwise approach

These algorithms try to directly optimize the value of one of the above evaluation measures, averaged over all queries in the training data. This is often difficult in practice because most evaluation measures are not continuous functions with respect to ranking model's parameters, and so continuous approximations or bounds on evaluation measures have to be used. For example the SoftRank algorithm. [16] LambdaMART is a pairwise algorithm which has been empirically shown to approximate listwise objective functions. [17]

List of methods

A partial list of published learning-to-rank algorithms is shown below with years of first publication of each method:

YearNameTypeNotes
1989OPRF [18] pointwisePolynomial regression (instead of machine learning, this work refers to pattern recognition, but the idea is the same)
1992SLR [19] pointwiseStaged logistic regression
1994NMOpt [20] listwiseNon-Metric Optimization
1999 MART (Multiple Additive Regression Trees) pairwise
2000 Ranking SVM (RankSVM) pairwiseA more recent exposition is in, [3] which describes an application to ranking using clickthrough logs.
2002Pranking [21] pointwiseOrdinal regression.
2003 RankBoost pairwise
2005 RankNet pairwise
2006 IR-SVM pairwiseRanking SVM with query-level normalization in the loss function.
2006 LambdaRank pairwise/listwiseRankNet in which pairwise loss function is multiplied by the change in the IR metric caused by a swap.
2007 AdaRank listwise
2007 FRank pairwiseBased on RankNet, uses a different loss function - fidelity loss.
2007 GBRank pairwise
2007 ListNet listwise
2007 McRank pointwise
2007 QBRank pairwise
2007 RankCosine listwise
2007RankGP [22] listwise
2007 RankRLS pairwise

Regularized least-squares based ranking. The work is extended in [23] to learning to rank from general preference graphs.

2007 SVMmap listwise
2008 LambdaSMART/LambdaMART pairwise/listwiseWinning entry in the Yahoo Learning to Rank competition in 2010, using an ensemble of LambdaMART models. Based on MART (1999) [24] “LambdaSMART”, for Lambda-submodel-MART, or LambdaMART for the case with no submodel.
2008 ListMLE listwiseBased on ListNet.
2008 PermuRank listwise
2008 SoftRank listwise
2008 Ranking Refinement [25] pairwiseA semi-supervised approach to learning to rank that uses Boosting.
2008 SSRankBoost [26] pairwiseAn extension of RankBoost to learn with partially labeled data (semi-supervised learning to rank)
2008 SortNet [27] pairwiseSortNet, an adaptive ranking algorithm which orders objects using a neural network as a comparator.
2009 MPBoost pairwiseMagnitude-preserving variant of RankBoost. The idea is that the more unequal are labels of a pair of documents, the harder should the algorithm try to rank them.
2009 BoltzRank listwiseUnlike earlier methods, BoltzRank produces a ranking model that looks during query time not just at a single document, but also at pairs of documents.
2009 BayesRank listwiseA method combines Plackett-Luce Model and neural network to minimize the expected Bayes risk, related to NDCG, from the decision-making aspect.
2010 NDCG Boost [28] listwiseA boosting approach to optimize NDCG.
2010 GBlend pairwiseExtends GBRank to the learning-to-blend problem of jointly solving multiple learning-to-rank problems with some shared features.
2010 IntervalRank pairwise & listwise
2010 CRR pointwise & pairwiseCombined Regression and Ranking. Uses stochastic gradient descent to optimize a linear combination of a pointwise quadratic loss and a pairwise hinge loss from Ranking SVM.
2014 LCR pairwiseApplied local low-rank assumption on collaborative ranking. Received best student paper award at WWW'14.
2015 FaceNet pairwiseRanks face images with the triplet metric via deep convolutional network.
2016 XGBoost pairwiseSupports various ranking objectives and evaluation metrics.
2017 ES-Rank listwiseEvolutionary Strategy Learning to Rank technique with 7 fitness evaluation metrics
2018 DLCM [29] listwiseA multi-variate ranking function that encodes multiple items from an initial ranked list (local context) with a recurrent neural network and create result ranking accordingly.
2018 PolyRank [30] pairwiseLearns simultaneously the ranking and the underlying generative model from pairwise comparisons.
2018 FATE-Net/FETA-Net [31] listwiseEnd-to-end trainable architectures, which explicitly take all items into account to model context effects.
2019 FastAP [32] listwiseOptimizes Average Precision to learn deep embeddings
2019 Mulberry listwise & hybridLearns ranking policies maximizing multiple metrics across the entire dataset
2019 DirectRanker pairwiseGeneralisation of the RankNet architecture
2019 GSF [33] listwiseA permutation-invariant multi-variate ranking function that encodes and ranks items with groupwise scoring functions built with deep neural networks.
2020 RaMBO [34] listwiseOptimizes rank-based metrics using blackbox backpropagation [35]
2020 PRM [36] pairwiseTransformer network encoding both the dependencies among items and the interactions

between the user and items

2020 SetRank [37] listwiseA permutation-invariant multi-variate ranking function that encodes and ranks items with self-attention networks.
2021 PiRank [38] listwiseDifferentiable surrogates for ranking able to exactly recover the desired metrics and scales favourably to large list sizes, significantly improving internet-scale benchmarks.
2022 SAS-Rank listwiseCombining Simulated Annealing with Evolutionary Strategy for implicit and explicit learning to rank from relevance labels
2022VNS-Rank listwiseVariable Neighborhood Search in 2 Novel Methodologies in AI for Learning to Rank
2022 VNA-Rank listwiseCombining Simulated Annealing with Variable Neighbourhood Search for Learning to Rank
2023 GVN-Rank listwiseCombining Gradient Ascent with Variable Neighbourhood Search for Learning to Rank

Note: as most supervised learning to Rank algorithms can be applied to pointwise, pairwise and listwise case, only those methods which are specifically designed with ranking in mind are shown above.

History

Norbert Fuhr introduced the general idea of MLR in 1992, describing learning approaches in information retrieval as a generalization of parameter estimation; [39] a specific variant of this approach (using polynomial regression) had been published by him three years earlier. [18] Bill Cooper proposed logistic regression for the same purpose in 1992 [19] and used it with his Berkeley research group to train a successful ranking function for TREC. Manning et al. [40] suggest that these early works achieved limited results in their time due to little available training data and poor machine learning techniques.

Several conferences, such as NIPS, SIGIR and ICML had workshops devoted to the learning-to-rank problem since mid-2000s (decade).

Practical usage by search engines

Commercial web search engines began using machine learned ranking systems since the 2000s (decade). One of the first search engines to start using it was AltaVista (later its technology was acquired by Overture, and then Yahoo), which launched a gradient boosting-trained ranking function in April 2003. [41] [42]

Bing's search is said to be powered by RankNet algorithm, [43] [ when? ] which was invented at Microsoft Research in 2005.

In November 2009 a Russian search engine Yandex announced [44] that it had significantly increased its search quality due to deployment of a new proprietary MatrixNet algorithm, a variant of gradient boosting method which uses oblivious decision trees. [45] Recently they have also sponsored a machine-learned ranking competition "Internet Mathematics 2009" [46] based on their own search engine's production data. Yahoo has announced a similar competition in 2010. [47]

As of 2008, Google's Peter Norvig denied that their search engine exclusively relies on machine-learned ranking. [48] Cuil's CEO, Tom Costello, suggests that they prefer hand-built models because they can outperform machine-learned models when measured against metrics like click-through rate or time on landing page, which is because machine-learned models "learn what people say they like, not what people actually like". [49]

In January 2017, the technology was included in the open source search engine Apache Solr. [50] It is also available in the open source OpenSearch and the source-available Elasticsearch. [51] [52] These implementations make learning to rank widely accessible for enterprise search.

Vulnerabilities

Similar to recognition applications in computer vision, recent neural network based ranking algorithms are also found to be susceptible to covert adversarial attacks, both on the candidates and the queries. [53] With small perturbations imperceptible to human beings, ranking order could be arbitrarily altered. In addition, model-agnostic transferable adversarial examples are found to be possible, which enables black-box adversarial attacks on deep ranking systems without requiring access to their underlying implementations. [53] [54]

Conversely, the robustness of such ranking systems can be improved via adversarial defenses such as the Madry defense. [55]

See also

Related Research Articles

Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.

Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text. A matrix containing word counts per document is constructed from a large piece of text and a mathematical technique called singular value decomposition (SVD) is used to reduce the number of rows while preserving the similarity structure among columns. Documents are then compared by cosine similarity between any two columns. Values close to 1 represent very similar documents while values close to 0 represent very dissimilar documents.

<span class="mw-page-title-main">Automatic image annotation</span>

Automatic image annotation is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database.

Relevance feedback is a feature of some information retrieval systems. The idea behind relevance feedback is to take the results that are initially returned from a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types of feedback: explicit feedback, implicit feedback, and blind or "pseudo" feedback.

Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values.

Query expansion (QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding. In the context of search engines, query expansion involves evaluating a user's input and expanding the search query to match additional documents. Query expansion involves techniques such as:

Human–computer information retrieval (HCIR) is the study and engineering of information retrieval techniques that bring human intelligence into the search process. It combines the fields of human-computer interaction (HCI) and information retrieval (IR) and creates systems that improve search by taking into account the human context, or through a multi-step search process that provides the opportunity for human feedback.

GenieKnows Inc. was a privately owned vertical search engine company based in Halifax, Nova Scotia. It was started by Rami Hamodah who also started SwiftlyLabs.com and Salesboom.com. Like many internet search engines, its revenue model centers on an online advertising platform and B2B transactions. It focuses on a set of search markets, or verticals, including health search, video games search, and local business directory search.

A Web query topic classification/categorization is a problem in information science. The task is to assign a Web search query to one or more predefined categories, based on its topics. The importance of query classification is underscored by many services provided by Web search. A direct application is to provide better search result pages for users with interests of different categories. For example, the users issuing a Web query "apple" might expect to see Web pages related to the fruit apple, or they may prefer to see products or news related to the computer company. Online advertisement services can rely on the query classification results to promote different products more accurately. Search result pages can be grouped according to the categories predicted by a query classification algorithm. However, the computation of query classification is non-trivial. Different from the document classification tasks, queries submitted by Web search users are usually short and ambiguous; also the meanings of the queries are evolving over time. Therefore, query topic classification is much more difficult than traditional document classification tasks.

Discounted cumulative gain (DCG) is a measure of ranking quality which is often presented in its normalized and more easily interpretable form, known as Normalized DCG (nDCG or NDCG). In information retrieval, NDCG is often used to measure effectiveness of search engine algorithms and related applications. Using a graded relevance scale of documents in a search-engine result set, DCG measures the usefulness, or gain, of a document based on its position in the result list. To compute NDCG, the gain is accumulated from the top of the result list to the bottom, with the gain of each result discounted at lower ranks. The normalized version corrects for the fact that different queries may have different numbers of relevant results.

Ranking of query is one of the fundamental problems in information retrieval (IR), the scientific/engineering discipline behind search engines. Given a query q and a collection D of documents that match the query, the problem is to rank, that is, sort, the documents in D according to some criterion so that the "best" results appear early in the result list displayed to the user. Ranking in terms of information retrieval is an important concept in computer science and is used in many different applications such as search engine queries and recommender systems. A majority of search engines use ranking algorithms to provide users with accurate and relevant results.

XML retrieval, or XML information retrieval, is the content-based retrieval of documents structured with XML. As such it is used for computing relevance of XML documents.

The Generalized vector space model is a generalization of the vector space model used in information retrieval. Wong et al. presented an analysis of the problems that the pairwise orthogonality assumption of the vector space model (VSM) creates. From here they extended the VSM to the generalized vector space model (GVSM).

The query likelihood model is a language model used in information retrieval. A language model is constructed for each document in the collection. It is then possible to rank each document by the probability of specific documents given a query. This is interpreted as being the likelihood of a document being relevant given a query.

In machine learning, a ranking SVM is a variant of the support vector machine algorithm, which is used to solve certain ranking problems. The ranking SVM algorithm was published by Thorsten Joachims in 2002. The original purpose of the algorithm was to improve the performance of an internet search engine. However, it was found that ranking SVM also can be used to solve other problems such as Rank SIFT.

Similarity learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but the goal is to learn a similarity function that measures how similar or related two objects are. It has applications in ranking, in recommendation systems, visual identity tracking, face verification, and speaker verification.

Evaluation measures for an information retrieval (IR) system assess how well an index, search engine or database returns results from a collection of resources that satisfy a user's query. They are therefore fundamental to the success of information systems and digital platforms. The success of an IR system may be judged by a range of criteria including relevance, speed, user satisfaction, usability, efficiency and reliability. However, the most important factor in determining a system's effectiveness for users is the overall relevance of results retrieved in response to a query. Evaluation measures may be categorised in various ways including offline or online, user-based or system-based and include methods such as observed user behaviour, test collections, precision and recall, and scores from prepared benchmark test sets.

Query understanding is the process of inferring the intent of a search engine user by extracting semantic meaning from the searcher’s keywords. Query understanding methods generally take place before the search engine retrieves and ranks results. It is related to natural language processing but specifically focused on the understanding of search queries. Query understanding is at the heart of technologies like Amazon Alexa, Apple's Siri. Google Assistant, IBM's Watson, and Microsoft's Cortana.

BitFunnel is the search engine indexing algorithm and a set of components used in the Bing search engine, which were made open source in 2016. BitFunnel uses bit-sliced signatures instead of an inverted index in an attempt to reduce operations cost.

Learned sparse retrieval or sparse neural search is an approach to text search which uses a sparse vector representation of queries and documents. It borrows techniques both from lexical bag-of-words and vector embedding algorithms, and is claimed to perform better than either alone. The best-known sparse neural search systems are SPLADE and its successor SPLADE v2. Others include DeepCT, uniCOIL, EPIC, DeepImpact, TILDE and TILDEv2, Sparta, SPLADE-max, and DistilSPLADE-max.

References

  1. 1 2 Tie-Yan Liu (2009), "Learning to Rank for Information Retrieval", Foundations and Trends in Information Retrieval, 3 (3): 225–331, doi:10.1561/1500000016, ISBN   978-1-60198-244-5 . Slides from Tie-Yan Liu's talk at WWW 2009 conference are available online Archived 2017-08-08 at the Wayback Machine
  2. Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012) Foundations of Machine Learning, The MIT Press ISBN   9780262018258.
  3. 1 2 Joachims, T. (2002), "Optimizing Search Engines using Clickthrough Data" (PDF), Proceedings of the ACM Conference on Knowledge Discovery and Data Mining, archived (PDF) from the original on 2009-12-29, retrieved 2009-11-11
  4. Joachims T.; Radlinski F. (2005), "Query Chains: Learning to Rank from Implicit Feedback" (PDF), Proceedings of the ACM Conference on Knowledge Discovery and Data Mining, arXiv: cs/0605035 , Bibcode:2006cs........5035R, archived (PDF) from the original on 2011-07-27, retrieved 2009-12-19
  5. B. Cambazoglu; H. Zaragoza; O. Chapelle; J. Chen; C. Liao; Z. Zheng; J. Degenhardt., "Early exit optimizations for additive machine learned ranking systems" (PDF), WSDM '10: Proceedings of the Third ACM International Conference on Web Search and Data Mining, 2010., archived from the original (PDF) on 2019-08-28, retrieved 2009-12-23
  6. Broder A.; Carmel D.; Herscovici M.; Soffer A.; Zien J. (2003), "Efficient query evaluation using a two-level retrieval process", Proceedings of the twelfth international conference on Information and knowledge management (PDF), pp. 426–434, doi:10.1145/956863.956944, ISBN   978-1-58113-723-1, S2CID   2432701, archived from the original (PDF) on 2009-05-21, retrieved 2009-12-15
  7. 1 2 Manning C.; Raghavan P.; Schütze H. (2008), Introduction to Information Retrieval, Cambridge University Press. Section 7.1 Archived 2009-07-19 at the Wayback Machine
  8. 1 2 Kevin K. Duh (2009), Learning to Rank with Partially-Labeled Data (PDF), archived (PDF) from the original on 2011-07-20, retrieved 2009-12-27
  9. Yuanhua Lv, Taesup Moon, Pranam Kolari, Zhaohui Zheng, Xuanhui Wang, and Yi Chang, Learning to Model Relatedness for News Recommendation Archived 2011-08-27 at the Wayback Machine , in International Conference on World Wide Web (WWW), 2011.
  10. Richardson, M.; Prakash, A.; Brill, E. (2006). "Beyond PageRank: Machine Learning for Static Ranking" (PDF). Proceedings of the 15th International World Wide Web Conference. pp. 707–715. Archived (PDF) from the original on 2009-08-15. Retrieved 2009-11-18.
  11. "Archived copy". Archived from the original on 2011-01-04. Retrieved 2009-12-14.{{cite web}}: CS1 maint: archived copy as title (link)
  12. Olivier Chapelle; Donald Metzler; Ya Zhang; Pierre Grinspan (2009), "Expected Reciprocal Rank for Graded Relevance" (PDF), CIKM, archived from the original (PDF) on 2012-02-24
  13. Gulin A.; Karpovich P.; Raskovalov D.; Segalovich I. (2009), "Yandex at ROMIP'2009: optimization of ranking algorithms by machine learning methods" (PDF), Proceedings of ROMIP'2009: 163–168, archived (PDF) from the original on 2009-11-22, retrieved 2009-11-13 (in Russian)
  14. Tax, Niek; Bockting, Sander; Hiemstra, Djoerd (2015), "A cross-benchmark comparison of 87 learning to rank methods" (PDF), Information Processing & Management, 51 (6): 757–772, doi:10.1016/j.ipm.2015.07.002, S2CID   22782599, archived from the original (PDF) on 2017-08-09, retrieved 2017-10-15
  15. Burges, Chris J. C.; Shaked, Tal; Renshaw, Erin; Lazier, Ari; Deeds, Matt; Hamilton, Nicole; Hullender, Greg (1 August 2005). "Learning to Rank using Gradient Descent". Archived from the original on 26 February 2021. Retrieved 31 March 2021.{{cite journal}}: Cite journal requires |journal= (help)
  16. Taylor, M.J., Guiver, J., Robertson, S.E., & Minka, T.P. (2008). SoftRank: optimizing non-smooth rank metrics. Web Search and Data Mining.
  17. Burges, Chris J. C. (2010-06-23). "From RankNet to LambdaRank to LambdaMART: An Overview".{{cite journal}}: Cite journal requires |journal= (help)
  18. 1 2 Fuhr, Norbert (1989), "Optimum polynomial retrieval functions based on the probability ranking principle", ACM Transactions on Information Systems, 7 (3): 183–204, doi: 10.1145/65943.65944 , S2CID   16632383
  19. 1 2 Cooper, William S.; Gey, Frederic C.; Dabney, Daniel P. (1992), "Probabilistic retrieval based on staged logistic regression", Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '92, pp. 198–210, doi:10.1145/133160.133199, ISBN   978-0897915236, S2CID   125993
  20. Bartell, Brian T.; Cottrell Garrison W.; Belew, Richard K. (1994), "Automatic Combination of Multiple Ranked Retrieval Systems", Sigir '94, pp. 173–181, doi:10.1007/978-1-4471-2099-5_18, ISBN   978-0387198897, S2CID   18606472, archived from the original on 2018-06-13, retrieved 2020-10-12
  21. "Pranking". 2001: 641–647. CiteSeerX   10.1.1.20.378 .{{cite journal}}: Cite journal requires |journal= (help)
  22. "RankGP". CiteSeerX   10.1.1.90.220 .{{cite journal}}: Cite journal requires |journal= (help)
  23. Pahikkala, Tapio; Tsivtsivadze, Evgeni; Airola, Antti; Järvinen, Jouni; Boberg, Jorma (2009), "An efficient algorithm for learning to rank from preference graphs", Machine Learning, 75 (1): 129–165, doi: 10.1007/s10994-008-5097-z .
  24. C. Burges. (2010). From RankNet to LambdaRank to LambdaMART: An Overview Archived 2017-11-10 at the Wayback Machine .
  25. Rong Jin, Hamed Valizadegan, Hang Li, Ranking Refinement and Its Application for Information Retrieval Archived 2012-04-06 at the Wayback Machine , in International Conference on World Wide Web (WWW), 2008.
  26. Massih-Reza Amini, Vinh Truong, Cyril Goutte, A Boosting Algorithm for Learning Bipartite Ranking Functions with Partially Labeled Data Archived 2010-08-02 at the Wayback Machine , International ACM SIGIR conference, 2008. The code Archived 2010-07-23 at the Wayback Machine is available for research purposes.
  27. Leonardo Rigutini, Tiziano Papini, Marco Maggini, Franco Scarselli, "SortNet: learning to rank by a neural-based sorting algorithm" Archived 2011-11-25 at the Wayback Machine , SIGIR 2008 workshop: Learning to Rank for Information Retrieval, 2008
  28. Hamed Valizadegan, Rong Jin, Ruofei Zhang, Jianchang Mao, Learning to Rank by Optimizing NDCG Measure Archived 2012-04-06 at the Wayback Machine , in Proceeding of Neural Information Processing Systems (NIPS), 2010.
  29. Ai, Qingyao; Bi, Keping; Jiafeng, Guo; Croft, W. Bruce (2018), "Learning a Deep Listwise Context Model for Ranking Refinement", The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 135–144, arXiv: 1804.05936 , doi: 10.1145/3209978.3209985 , ISBN   9781450356572, S2CID   4956076
  30. Davidov, Ori; Ailon, Nir; Oliveira, Ivo F. D. (2018). "A New and Flexible Approach to the Analysis of Paired Comparison Data". Journal of Machine Learning Research. 19 (60): 1–29. ISSN   1533-7928. Archived from the original on 2019-10-03. Retrieved 2019-09-17.
  31. Pfannschmidt, Karlson; Gupta, Pritha; Hüllermeier, Eyke (2018). "Deep Architectures for Learning Context-dependent Ranking Functions". arXiv: 1803.05796 [stat.ML].
  32. Fatih Cakir, Kun He, Xide Xia, Brian Kulis, Stan Sclaroff, Deep Metric Learning to Rank Archived 2019-05-14 at the Wayback Machine , In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
  33. Ai, Qingyao; Wang, Xuanhui; Bruch, Sebastian; Golbandi, Nadav; Bendersky, Michael; Najork, Marc (2019), "Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks", Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 85–92, arXiv: 1811.04415 , doi: 10.1145/3341981.3344218 , ISBN   9781450368810, S2CID   199441954
  34. Rolínek, Michal; Musil, Vít; Paulus, Anselm; Vlastelica, Marin; Michaelis, Claudio; Martius, Georg (2020-03-18). "Optimizing Rank-based Metrics with Blackbox Differentiation". arXiv: 1912.03500 [cs.LG].
  35. Vlastelica, Marin; Paulus, Anselm; Musil, Vít; Martius, Georg; Rolínek, Michal (2019-12-04). "Differentiation of Blackbox Combinatorial Solvers". arXiv: 1912.02175 .{{cite journal}}: Cite journal requires |journal= (help)
  36. Liu, Weiwen; Liu, Qing; Tang, Ruiming; Chen, Junyang; He, Xiuqiang; Heng, Pheng Ann (2020-10-19). "Personalized Re-ranking with Item Relationships for E-commerce". Proceedings of the 29th ACM International Conference on Information & Knowledge Management. CIKM '20. Virtual Event, Ireland: Association for Computing Machinery. pp. 925–934. doi:10.1145/3340531.3412332. ISBN   978-1-4503-6859-9. S2CID   224281012. Archived from the original on 2021-10-17. Retrieved 2021-04-26.
  37. Pang, Liang; Xu, Jun; Ai, Qingyao; Lan, Yanyan; Cheng, Xueqi; Wen, Jirong (2020), "SetRank", Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 499–508, doi:10.1145/3397271.3401104, ISBN   9781450380164, S2CID   241534531
  38. Swezey, Robin; Grover, Aditya; Charron, Bruno; Ermon, Stefano (2021-11-27). "PiRank: Scalable Learning To Rank via Differentiable Sorting". Advances in Neural Information Processing Systems. NeurIPS '21. Virtual Event, Ireland. 34. arXiv: 2012.06731 .
  39. Fuhr, Norbert (1992), "Probabilistic Models in Information Retrieval", Computer Journal, 35 (3): 243–255, doi: 10.1093/comjnl/35.3.243
  40. Manning C.; Raghavan P.; Schütze H. (2008), Introduction to Information Retrieval, Cambridge University Press. Sections 7.4 Archived 2009-07-21 at the Wayback Machine and 15.5 Archived 2010-05-09 at the Wayback Machine
  41. Jan O. Pedersen. The MLR Story Archived 2011-07-13 at the Wayback Machine
  42. U.S. Patent 7,197,497
  43. "Bing Search Blog: User Needs, Features and the Science behind Bing". Archived from the original on 2009-11-25. Retrieved 2009-11-19.
  44. Yandex corporate blog entry about new ranking model "Snezhinsk" Archived 2012-03-01 at the Wayback Machine (in Russian)
  45. The algorithm wasn't disclosed, but a few details were made public in Archived 2010-06-01 at the Wayback Machine and Archived 2010-06-01 at the Wayback Machine .
  46. "Yandex's Internet Mathematics 2009 competition page". Archived from the original on 2015-03-17. Retrieved 2009-11-11.
  47. "Yahoo Learning to Rank Challenge". Archived from the original on 2010-03-01. Retrieved 2010-02-26.
  48. Rajaraman, Anand (2008-05-24). "Are Machine-Learned Models Prone to Catastrophic Errors?". Archived from the original on 2010-09-18. Retrieved 2009-11-11.
  49. Costello, Tom (2009-06-26). "Cuil Blog: So how is Bing doing?". Archived from the original on 2009-06-27.
  50. "How Bloomberg Integrated Learning-to-Rank into Apache Solr | Tech at Bloomberg". Tech at Bloomberg. 2017-01-23. Archived from the original on 2017-03-01. Retrieved 2017-02-28.
  51. "Learning to Rank for Amazon OpenSearch Service - Amazon OpenSearch Service". docs.aws.amazon.com. Retrieved 2023-09-22.
  52. "Elasticsearch Learning to Rank: the documentation — Elasticsearch Learning to Rank documentation". elasticsearch-learning-to-rank.readthedocs.io. Retrieved 2023-09-22.
  53. 1 2 Zhou, Mo; Niu, Zhenxing; Wang, Le; Zhang, Qilin; Hua, Gang (2020). "Adversarial Ranking Attack and Defense". arXiv: 2002.11293v2 [cs.CV].
  54. Li, Jie; Ji, Rongrong; Liu, Hong; Hong, Xiaopeng; Gao, Yue; Tian, Qi (2019). "Universal Perturbation Attack Against Image Retrieval". International Conference on Computer Vision (ICCV 2019): 4899–4908. arXiv: 1812.00552 . Archived from the original on 2020-07-06. Retrieved 2020-07-04.
  55. Madry, Aleksander; Makelov, Aleksandar; Schmidt, Ludwig; Tsipras, Dimitris; Vladu, Adrian (2017-06-19). "Towards Deep Learning Models Resistant to Adversarial Attacks". arXiv: 1706.06083v4 [stat.ML].
Competitions and public datasets