改进的RankSVM在文档检索中的应用与挑战

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本文探讨了将学习到排名（Learning to Rank, LTR）技术应用于文档检索领域，特别是针对Ranking SVM方法的应用。Ranking SVM作为一种经典的LTR方法，其核心在于通过优化模型来预测和排列查询结果中的文档顺序，以提升检索系统的召回率和用户体验。在将其应用于文档检索时，作者指出了两个关键因素： 1. 准确的Top-K排序：对于一个成功的IR系统来说，将真正相关的文档排在搜索结果的前几项至关重要。因此，在使用Ranking SVM进行训练时，必须确保模型能够准确地识别并按照相关性对文档进行高置信度的排序。这需要精心设计的特征工程和合适的损失函数，以便模型能够捕捉文档之间的相关性和重要性。 2. 处理查询间差异：每个查询可能有不同的相关文档数量，这是文档检索中的一个重要特性。模型不能仅仅针对包含大量相关文档的查询进行优化，因为这可能导致对文档稀疏或相关文档较少的查询表现不佳。因此，一个好的Ranking SVM模型需要具备适应不同查询复杂性的能力，避免过度偏向于某些特定情况。在以前的研究中，尽管Ranking SVM和其他类似方法已被应用于文档检索，但似乎并未充分考虑到这两个关键因素。为了克服这一问题，本文提出了一种新颖的策略，可能是通过改进的特征选择、动态调整模型参数或者引入适应性机制来平衡模型的泛化能力和对各种查询规模的处理。通过实验和案例分析，作者旨在提供一种有效的方法，使得Ranking SVM在文档检索任务中既能保证精确度又能适应多样的查询需求，从而提高整体的检索性能。

Adapting Ranking SVM to Document Retrieval

Yunbo CAO

, Jun XU

, Tie-Yan LIU

, Hang LI

, Yalou HUANG

, Hsiao-Wuen HON

Microsoft Research Asia,

No.49 Zhichun Road, Haidian District

Beijing, China, 100080

{yunbo.cao, tyliu, hangli, hon}@microsoft.com

College of Software, Nankai University,

No.94 Weijin Road, Nankai District

Tianjin, China, 300071

nkxj@hotmail.com, yellow@nankai.edu.cn

ABSTRACT

The paper is concerned with applying learning to rank to

document retrieval. Ranking SVM is a typical method of learning

to rank. We point out that there are two factors one must consider

when applying Ranking SVM, in general a “learning to rank”

method, to document retrieval. First, correctly ranking documents

on the top of the result list is crucial for an Information Retrieval

system. One must conduct training in a way that such ranked

results are accurate. Second, the number of relevant documents

can vary from query to query. One must avoid training a model

biased toward queries with a large number of relevant documents.

Previously, when existing methods that include Ranking SVM

were applied to document retrieval, none of the two factors was

taken into consideration. We show it is possible to make

modifications in conventional Ranking SVM, so it can be better

used for document retrieval. Specifically, we modify the “Hinge

Loss” function in Ranking SVM to deal with the problems

described above. We employ two methods to conduct

optimization on the loss function: gradient descent and quadratic

programming. Experimental results show that our method,

referred to as Ranking SVM for IR, can outperform the

conventional Ranking SVM and other existing methods for

document retrieval on two datasets.

Categories and Subject Descriptors

H.3.3 [Information Search and Retrieval]: Information Search

and Retrieval – Retrieval Models

General Terms

Algorithms, Experimentation, Theory

Keywords

Information retrieval, loss function, Ranking SVM

1. INTRODUCTION

Ranking functions in document retrieval traditionally use a small

number of features (e.g., term frequency, inversed document

frequency, and document length), which makes it possible to

empirically tune ranking parameters [20]. Recently, however, a

growing number of features such as structural features, title text,

and anchor text, and query-independent features (e.g., PageRank

and URL length) have proved useful in document retrieval while

empirical tuning of ranking functions has become increasingly

difficult.

Fortunately, in recent years more and more human-judged

document retrieval results have become available. This makes it

possible to employ supervised learning methodologies in the

tuning of ranking functions. Many such efforts have been made

using these approaches.

In one of such effort, document retrieval is formalized as

classification of documents into two categories: relevant and

irrelevant. Nallapati [12], for example, formalizes document

retrieval as binary classification and solves the classification

problem using Support Vector Machines (SVM) and Maximum

Entropy (ME).

In another approach, document retrieval is formalized as a

“learning to rank” problem in which documents are mapped into

several ordered categories (ranks). OHSUMED [9] is a data

collection that contains multiple data categories or ranks:

definitely relevant, partially relevant, and irrelevant. Herbrich et

al. [8], for instance, propose a method of learning to rank on the

basis of SVM and apply their method to document retrieval. We

refer to their method as Ranking SVM (or conventional Ranking

SVM) in this paper. Specifically, Ranking SVM formalizes

learning to rank as a problem of classifying instance pairs into

two categories (correctly ranked and incorrectly ranked). Other

methods within this approach have also been proposed [1, 19, 24].

We explore the problem of applying learning to rank to

document retrieval and propose a new learning method on the

basis of Ranking SVM. We refer to the method as Ranking SVM

for IR.

We note two important factors to take into general

consideration when applying Ranking SVM in a learning method

for ranking documents being retrieved. Unfortunately, they are

ignored in the existing methods, such as Ranking SVM.

(1) To have high accuracy on top-ranked documents is crucial

for an IR system. Analysis on click-through data from search

engines shows that users usually click on top-ranked documents

among returned search results [16, 17, 18]. The Normalized

Discounted Cumulated Gain (NDCG) measure [10] used in

evaluation of document retrieval also reflects this preference.

Therefore, it is necessary to perform training so that the top-

ranked results (equivalently the ranked results with regard to the

highest ranks) are generally accurate. However, in existing

learning methods such as Ranking SVM, the losses (penalties) of

incorrect ranking between higher ranks and lower ranks and

incorrect ranking among lower ranks are defined the same.

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copies bear this notice and the full citation on the first page. To copy

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requires prior specific permission and/or a fee.

SIGIR’06, August 6-11, 2006, Seattle, Washington, USA.

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