SVM驱动的Learning to Rank简介：信息检索与NLP中的关键技术

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"李航博士的《Learning to Rank：一个简要介绍》是关于排名学习在信息检索、自然语言处理和数据挖掘领域中的重要研究。该论文发表于2011年IEICE Transactions on Information and Systems的特别专题——信息基于归纳科学与机器学习，旨在概述机器学习技术在排名任务中的训练模型方法。排名问题在信息检索中的应用非常广泛，包括文档检索、专家搜索、定义搜索以及协同过滤等。学习到排名（Learning to Rank）的关键在于，它利用机器学习策略来解决如何根据用户的需求或查询意图，自动为相关的信息资源进行排序的问题。这涉及到对用户行为、查询语义和文档特征的深入理解，以便生成更符合用户期望的排序结果。论文重点讨论了学习到排名的核心问题，这些问题涵盖了如何设计有效的特征表示、如何选择合适的模型结构、以及如何处理噪声和不确定性。支持向量机（SVM）作为一种强大的机器学习工具，在文中被详细介绍，因为它在许多学习到排名算法中得到了广泛应用。SVM通过构建高效的分类边界，能够有效地将文档映射到一个有序的得分空间，从而实现排序。此外，李航博士还探讨了现有的一些学习到排名方法，这些方法可能包括基于线性模型的方法如Pointwise、Pairwise和Listwise评估，以及更复杂的模型如RankNet、LambdaMART和ListMLE等。这些方法各有优缺点，适用于不同的场景和需求。未来的研究方向可能集中在提升模型的解释性、实时性、个性化以及适应复杂环境变化上。同时，随着深度学习的发展，神经网络在排名学习中的作用也越来越受到关注，如使用深度神经网络来捕捉更深层次的语义关系。这篇论文为读者提供了一个全面的视角，帮助理解学习到排名的基本原理，引导读者进入这个领域进行深入研究，对于从事信息检索、自然语言处理和数据挖掘的工程师和研究人员具有重要的参考价值。"

IEICE TRANS. INF. & SYST., VOL.E94–D, NO.10 OCTOBER 2011

PAPER

Sp ecial Section on Information-Based Induction Sciences and Machine Learning

A Short Introduction to Learning to Rank

Hang LI

†

, Nonmember

SUMMARY Learning to rank refers to machine learning

techniques for training the model in a ranking task. Learning

to rank is useful for many applications in Information Retrieval,

Natural Language Processing, and Data Mining. Intensive stud-

ies have been conducted on the problem and signiﬁcant progress

has been made [1], [2]. This short paper gives an introduction

to learning to rank, and it speciﬁcally explains the fundamen-

tal problems, existing approaches, and future work of learning to

rank. Several learning to rank methods using SVM techniques

are described in details.

key words: Learning to rank, information retrieval, natural

language processing, SVM

1. Ranking Problem

Learning to rank can be employed in a wide variety

of applications in Information Retrieval (IR), Natural

Language Processing (NLP), and Data Mining (DM).

Typical applications are document retrieval, expert

search, deﬁnition search, collaborative ﬁltering, ques-

tion answering, keyphrase extraction, document sum-

marization, and machine translation [2]. Without loss

of generality, we take document retrieval as example in

this article.

Document retrieval is a task as follows (Fig. 1).

The system maintains a collection of documents. Given

a query, the system retrieves documents containing the

query words from the collection, ranks the documents,

and returns the top ranked documents. The ranking

task is performed by using a ranking model f (q, d ) to

sort the documents, where q denotes a query and d

denotes a document.

Traditionally, the ranking model f(q, d) is created

without training. In the BM25 model, for example, it

is assumed that f(q, d) is represented by a conditional

probability distribution P (r|q, d) where r takes on 1 or

0 as value and denotes being relevant or irreverent, and

q and d denote a query and a document respectively. In

Language Model for IR (LMIR), f (q, d) is represented

as a conditional probability distribution P (q|d). The

probability models can be calculated with the words

appearing in the query and do cument, and thus no

training is needed (only tuning of a small number of

parameters is necessary) [3].

Manuscript received December 31, 2010.

Manuscript revised June 1, 2011.

†

The author is with Microsoft Research Asia

DOI: 10.1587/transinf.E94.D.1

documents

{

}

dddD ,,,

ranking based on

relevance

Retrieval

System

query

ranking of documents

Fig. 1 Document Retrieval

A new trend has recently arisen in document re-

trieval, particularly in web search, that is, to employ

machine learning techniques to automatically construct

the ranking model f (q, d). This is motivated by a num-

ber of facts. At web search, there are many signals

which can represent relevance, for example, the anchor

texts and PageRank score of a web page. Incorporating

such information into the ranking model and automat-

ically constructing the ranking model using machine

learning techniques becomes a natural choice. In web

search engines, a large amount of search log data, such

as click through data, is accumulated. This makes it

possible to derive training data from search log data

and automatically create the ranking model. In fact,

learning to rank has become one of the key technolo-

gies for modern web search.

We describe a number of issues in learning for rank-

ing, including training and testing, data labeling, fea-

ture construction, evaluation, and relations with ordi-

nal classiﬁcation.

1.1 Training and Testing

Learning to rank is a supervised learning task and thus

has training and testing phases (see Fig. 2).

The training data consists of queries and docu-

ments. Each query is associated with a number of docu-

ments. The relevance of the documents with respect to

the query is also given. The relevance information can

be represented in several ways. Here, we take the most

widely used approach and assume that the relevance of

a document with respect to a query is represented by

 2011 The Institute of Electronics, Information and Communication Engineers

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