{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Ransaka Ravihara",
  "title_listed": "What Is Learning to Rank: A Beginner's Guide to Learning to Rank Methods",
  "keywords": "Learning to Rank,排序学习",
  "domain": "机器学习",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/what-is-learning-to-rank-a-beginners-guide-to-learning-to-rank-methods-23bbb99ef38c/",
  "publish_date": "2023-01",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "d32dd2b6108d",
  "collected_at": "2026-10-01T23:25:02",
  "title": "What Is Learning to Rank: A Beginner's Guide to Learning to Rank Methods | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# What Is Learning to Rank: A Beginner's Guide to Learning to Rank Methods\nA guide on how to approach LTR problems in Machine Learning\n[Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\nJanuary 17, 20236 min read\n![Photo by Possessed Photography on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1QmnsbqG7HIxPSYM3DYUgRg@2x.jpeg)\nPhoto by Possessed Photography on Unsplash\n### Introduction\nThis article will discuss what exactly a Learning to Rank is. Before diving deep into the inner workings, let's quickly catch up on the basic concepts required to understand.\nFirst, let's discover the Learning to Rank's central intuition. In machine learning, Learning to Rank (LTR) belongs to supervised machine learning, where we need a historical dataset to train the model. But when I started learning about the Learning to Rank concept, my first confusion was how I discriminated between traditional machine learning and LTR. Because if we are building a classification or regression model, our dependent and independent variables are pretty simple and make more sense. If we need to predict loan default for the given customer, we must plug specific feature vectors into the model learned function _f(x)._ It will return a single value or class probability for the customer being defaulted. But in the Learning to Rank model, this is quite different and confusing.\nLet's take a simple example.\nUser _A_ navigates to a website and inputs a query _q._ In this scenario, our system returned some documents, in other words, search results, like the one below.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/17xkYaLU01UGH3cSHjhtOyg.png)\nImage by Author\nIf we have a good ranking model, the relevance _(r)_ of these results should be _r(d1) > r(d2) > r(d3)._ Behind the scene, our model should return the relevance score for each document related to this query. Hence our model should learn a function that takes the query and document as a parameter and produce the relevance score for that particular query-document pair. Then we can do some computations and sort the documents in such a way high relevant documents receive a higher rank.\n* * *\n### Pointwise Ranking\nLet's discuss the one way that we can achieve this. First thing first, we need data. For simplicity, assume a hypothetical scenario where we have two queries, _q1, q2,_ and their associated list of documents [_d1,d2,d3], [d5,d6,d7],_ respectively.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1wI2klPpZkfBDlSZ7FRGYiQ.png)\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1VKV6SUqG5QJnryh_naRQqw.png)\nImage by Author\nGenerally, we know that _d1,d2,_ and _d3_ documents are relevant for _q1_ but not _q2,_ and the other way around. So we can populate the sample as follows.\n_sample 1 : d1,q1; label :1 sample 2 : d2,q1; label :1 sample 3 : d3,q1; label :1 sample 4 : d4,q2; label :1 sample 5: d5,q2; label :1 sample 6: d6,q2; label :1_\nIf you look at the above data, this problem is now simplified into a traditional classification/regression problem where we have a one-to-one mapping between inputs (_query_ and _document_ pair) and _labels._\n> Additional Fact\n> To solve this problem using machine learning you may perform feature engineering on query data and document data then feed it into the model and finally get predictions. Some ideal yet simple feature engineering would be retreving the number of times john and sushi appear in the query and document.\nBelieving or not, just now, you learned one of the three types of Learning to Rank methods called _**pointwise ranking**_.\n> The pointwise ranking is finding the function that returns each document's relevance given a query. It is named pointwise because each document is scored independently with the ground truth target values, just like traditional regression and classification tasks.\nLet's discuss the pros and cons of the pointwise ranking method. One advantage is its simplicity. But this simplicity comes with significant drawbacks. Such as,\n  1. Each instance is treated as an isolated point.\n  2. Explicit pointwise labels are required to create the training dataset.\n\n\n* * *\nTo overcome these challenges, we can use the _**Pairwise Ranking**_ method.\n### Pairwise Ranking\nHere, the goal is to define a ranking function to score each document based on a given query. The documents are then ranked in descending order of these scores, representing the relative relevance of the documents to the query.\n> In the learning process, several queries are provided, each with a pair of relevant documents. A ranking function is created using this training data so that the model can predict the relevant documents for future queries.\nLet's take the previous _sushi recipe_ example. Instead of considering one document for each query, like _pointwise ranking,_ we are now considering two documents per query. For example, we know _d1,d2,_ and _d3_ are relevant to _q1._ In this case, all possible query document pairs should be as follows.\n_sample 1: q1, (d1,d2) sample 2: q1, (d1,d3) sample 3: q1, (d2,d3)_\nWhere _(di,dj)_ denotes the order of documents _i_ and _j._ If we have explicit labels for how documents _i_ and _j_ should rank, we can derive labels for _(di,dj)._ Assume document _j_ has a relevance score of 3(highly relevant) and document _i_ have a relevance score of 0 (less relevant) concerning the query _q._ In our optimum rankings, document _i_ should rank higher than document _j_. Again this is simplified as a traditional classification task. But unlike _**pointwise ranking,**_ this method considers the ranking position.\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1KB4Uxf3CbHDDxnDWGoL57w.png)\nImage by Author\nThe pairwise loss function used in lambdarank objective in LightGBM. Using the LightGBM python library, we can train this state-of-art LTR method with few lines of code.\nSince we can simplify this into a classification task, we can use its known methodologies. It also takes the document order into the model. This has a few drawbacks as well. Even though learning considers the order of document pairs objective is not explicitly set to order the document; instead, it tries to reduce the classification error of document pairs. Furthermore, training can be very costly when the dataset contains large document and query pairs. Document count varies from query to query; this lead model is biased towards queries with large document pairs.\n### Listwise Ranking\nResearchers have introduced a novel listwise approach to overcome a few of the observed significant drawbacks in LTR.\n> In this approach, instead of considering pairs of documents, the list of ranked documents is taken into account, along with their relevance labels.\nTo get the relevance label per each document in the query, we can use human annotators or the number of clicks received for a particular document.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1A7KbFboe6XBED0bV6hXzVA.png)\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1PAamTSa4x5inxtBVDfmuiQ.png)\nImage by Author\nWe must add features based on query and document pairs in the learning phase. If _i represents the query's index_ and _j_ represents the document's index, we can define the feature vector as follows.\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1tqZUmJ_QYqkazsuqE3KpPg.png)\nImage by Author\nWith that, we can define the features for each document-query pair along with the ground truth labels.\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1cRFGdiHkyWRCQ1sGWG8mzQ.png)\nImage by Author\nFinally, we can denote training instance as,\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1QHe8nXEiCgJzE2ll2yx0_Q.png)\nImage by Author\nwhere _m_ is the number of queries in the dataset. Finally, Create a ranking function _f_ , which outputs a score for each feature vector, _x _ij_. Then obtain a list of scores, _z _i,_\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1MZiJZ1DKdQ51kNtRYw9-8w.png)\nImage by Author\nThe goal of learning is to minimize the total losses with respect to the training data. When a ranking applies, we can use a trained function to assign scores to new documents based on their feature vectors. The documents are then ranked in descending order of the scores.\nThanks to this learning process, it can learn relationships between items in a list, such as co-occurrence or dependencies. With this plus point, It expects a large amount of labeled data to learn the relationships between items in a list; thus, It can be computationally expensive to train and optimize. Furthermore, listwise can be a problem for new or niche domains with little or no labeled data available.\nLet's summarize what we have learned in this article.\n![comparison of LTR methods | Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1HSpS24LpYMVveOP1jkYHrw.jpeg)\ncomparison of LTR methods | Image by Author\n _Next in the Series:_[_How to evaluate a Learning to Rank Model_](https://medium.com/towards-data-science/how-to-evaluate-learning-to-rank-models-d12cadb99d47)\n> [**How to evaluate Learning to Rank Models**](https://towardsdatascience.com/how-to-evaluate-learning-to-rank-models-d12cadb99d47)\nThanks for Reading!\n_**References:**_\n  * [_From RankNet to LambdaRank to LambdaMART_](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf)\n  * [_Learning to Rank: From Pairwise Approach to Listwise Approach_](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-2007-40.pdf)\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657265628_qm714e.jpeg)\nWritten By\n## [Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\n[See all from Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\n[Data Science](https://towardsdatascience.com/tag/data-science/)[Learning To Rank](https://towardsdatascience.com/tag/learning-to-rank/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Math](https://towardsdatascience.com/tag/math/)[Supervised Learning](https://towardsdatascience.com/tag/supervised-learning/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fwhat-is-learning-to-rank-a-beginners-guide-to-learning-to-rank-methods-23bbb99ef38c%2F&title=What%20Is%20Learning%20to%20Rank%3A%20A%20Beginner's%20Guide%20to%20Learning%20to%20Rank%20Methods)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fwhat-is-learning-to-rank-a-beginners-guide-to-learning-to-rank-methods-23bbb99ef38c%2F&title=What%20Is%20Learning%20to%20Rank%3A%20A%20Beginner's%20Guide%20to%20Learning%20to%20Rank%20Methods)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fwhat-is-learning-to-rank-a-beginners-guide-to-learning-to-rank-methods-23bbb99ef38c%2F&text=What%20Is%20Learning%20to%20Rank%3A%20A%20Beginner's%20Guide%20to%20Learning%20to%20Rank%20Methods)\n\n\nTowards Data Science is a community publication. 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