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Proceedings ArticleDOI

Query chains: learning to rank from implicit feedback

21 Aug 2005-pp 239-248
TL;DR: A novel approach for using clickthrough data to learn ranked retrieval functions for web search results by using query chains to generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries.
Abstract: This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.

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Citations
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Book
Tie-Yan Liu1
27 Jun 2009
TL;DR: Three major approaches to learning to rank are introduced, i.e., the pointwise, pairwise, and listwise approaches, the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures are analyzed, and the performance of these approaches on the LETOR benchmark datasets is evaluated.
Abstract: This tutorial is concerned with a comprehensive introduction to the research area of learning to rank for information retrieval. In the first part of the tutorial, we will introduce three major approaches to learning to rank, i.e., the pointwise, pairwise, and listwise approaches, analyze the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures, evaluate the performance of these approaches on the LETOR benchmark datasets, and demonstrate how to use these approaches to solve real ranking applications. In the second part of the tutorial, we will discuss some advanced topics regarding learning to rank, such as relational ranking, diverse ranking, semi-supervised ranking, transfer ranking, query-dependent ranking, and training data preprocessing. In the third part, we will briefly mention the recent advances on statistical learning theory for ranking, which explain the generalization ability and statistical consistency of different ranking methods. In the last part, we will conclude the tutorial and show several future research directions.

2,515 citations

Proceedings ArticleDOI
20 Jun 2007
TL;DR: It is proposed that learning to rank should adopt the listwise approach in which lists of objects are used as 'instances' in learning, and introduces two probability models, respectively referred to as permutation probability and top k probability, to define a listwise loss function for learning.
Abstract: The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative filtering, and many other applications. Several methods for learning to rank have been proposed, which take object pairs as 'instances' in learning. We refer to them as the pairwise approach in this paper. Although the pairwise approach offers advantages, it ignores the fact that ranking is a prediction task on list of objects. The paper postulates that learning to rank should adopt the listwise approach in which lists of objects are used as 'instances' in learning. The paper proposes a new probabilistic method for the approach. Specifically it introduces two probability models, respectively referred to as permutation probability and top k probability, to define a listwise loss function for learning. Neural Network and Gradient Descent are then employed as model and algorithm in the learning method. Experimental results on information retrieval show that the proposed listwise approach performs better than the pairwise approach.

2,003 citations

Proceedings ArticleDOI
15 Aug 2005
TL;DR: It is concluded that clicks are informative but biased, and while this makes the interpretation of clicks as absolute relevance judgments difficult, it is shown that relative preferences derived from clicks are reasonably accurate on average.
Abstract: This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average.

1,484 citations


Additional excerpts

  • ...Initial findings are reported in [22]....

    [...]

Proceedings ArticleDOI
06 Aug 2006
TL;DR: In this paper, the authors show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithm by as much as 31% relative to the original performance.
Abstract: We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into the ranking process and explore the contributions of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.

1,119 citations

Proceedings ArticleDOI
09 Aug 2015
TL;DR: This paper presents a convolutional neural network architecture for reranking pairs of short texts, where the optimal representation of text pairs and a similarity function to relate them in a supervised way from the available training data are learned.
Abstract: Learning a similarity function between pairs of objects is at the core of learning to rank approaches In information retrieval tasks we typically deal with query-document pairs, in question answering -- question-answer pairs However, before learning can take place, such pairs needs to be mapped from the original space of symbolic words into some feature space encoding various aspects of their relatedness, eg lexical, syntactic and semantic Feature engineering is often a laborious task and may require external knowledge sources that are not always available or difficult to obtain Recently, deep learning approaches have gained a lot of attention from the research community and industry for their ability to automatically learn optimal feature representation for a given task, while claiming state-of-the-art performance in many tasks in computer vision, speech recognition and natural language processing In this paper, we present a convolutional neural network architecture for reranking pairs of short texts, where we learn the optimal representation of text pairs and a similarity function to relate them in a supervised way from the available training data Our network takes only words in the input, thus requiring minimal preprocessing In particular, we consider the task of reranking short text pairs where elements of the pair are sentences We test our deep learning system on two popular retrieval tasks from TREC: Question Answering and Microblog Retrieval Our model demonstrates strong performance on the first task beating previous state-of-the-art systems by about 3\% absolute points in both MAP and MRR and shows comparable results on tweet reranking, while enjoying the benefits of no manual feature engineering and no additional syntactic parsers

796 citations

References
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Proceedings ArticleDOI
23 Jul 2002
TL;DR: The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking.
Abstract: This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and expensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a theoretical perspective, this method is shown to be well-founded in a risk minimization framework. Furthermore, it is shown to be feasible even for large sets of queries and features. The theoretical results are verified in a controlled experiment. It shows that the method can effectively adapt the retrieval function of a meta-search engine to a particular group of users, outperforming Google in terms of retrieval quality after only a couple of hundred training examples.

4,453 citations

Posted ContentDOI
TL;DR: SVM light as discussed by the authors is an implementation of an SVM learner which addresses the problem of large-scale SVM training with many training examples on the shelf, which makes large scale SVM learning more practical.
Abstract: Training a support vector machine SVM leads to a quadratic optimization problem with bound constraints and one linear equality constraint Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner In particular, for large learning tasks with many training examples on the shelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements SVM light is an implementation of an SVM learner which addresses the problem of large tasks This chapter presents algorithmic and computational results developed for SVM light V 20, which make large-scale SVM training more practical The results give guidelines for the application of SVMs to large domains

4,145 citations

Journal ArticleDOI
Andrei Z. Broder1
01 Sep 2002
TL;DR: This taxonomy of web searches is explored and how global search engines evolved to deal with web-specific needs is discussed.
Abstract: Classic IR (information retrieval) is inherently predicated on users searching for information, the so-called "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me sites where I can perform a certain transaction, e.g. shop, download a file, or find a map). We explore this taxonomy of web searches and discuss how global search engines evolved to deal with web-specific needs.

2,094 citations


"Query chains: learning to rank from..." refers background in this paper

  • ...Thirty six undergraduate student volunteers were instructed to search for the answers to five navigational and five informational queries [5]....

    [...]

Proceedings Article
24 Jul 1998
TL;DR: RankBoost as discussed by the authors is an algorithm for combining preferences based on the boosting approach to machine learning, which can be applied to several applications, such as that of combining the results of different search engines, or the "collaborative filtering" problem of ranking movies for a user based on movie rankings provided by other users.
Abstract: We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences arises in several applications, such as that of combining the results of different search engines, or the "collaborative-filtering" problem of ranking movies for a user based on the movie rankings provided by other users. In this work, we begin by presenting a formal framework for this general problem. We then describe and analyze an efficient algorithm called RankBoost for combining preferences based on the boosting approach to machine learning. We give theoretical results describing the algorithm's behavior both on the training data, and on new test data not seen during training. We also describe an efficient implementation of the algorithm for a particular restricted but common case. We next discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different web search strategies, each of which is a query expansion for a given domain. The second experiment is a collaborative-filtering task for making movie recommendations.

1,888 citations

Proceedings ArticleDOI
15 Aug 2005
TL;DR: It is concluded that clicks are informative but biased, and while this makes the interpretation of clicks as absolute relevance judgments difficult, it is shown that relative preferences derived from clicks are reasonably accurate on average.
Abstract: This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average.

1,484 citations


"Query chains: learning to rank from..." refers background in this paper

  • ...Further analysis showed that this is usually the abstract immediately below the one clicked on [17]....

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  • ...However, implicit clickthrough data has been shown to be biased as it is relative to the retrieval function quality and ordering [15, 17]....

    [...]