A regression framework for learning ranking functions using relative relevance judgments

Zheng, Zhaohui; Chen, Keke; Sun, Guirong; Zha, Hongyuan

doi:10.1145/1277741.1277792

Cited by 162 publications

(96 citation statements)

References 21 publications

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“…RankSVM [12], RankNet [6], RankBoost [8] have been created to utilize preference data to enhance ranking. Our work is also related to the recent research of Ko et al [16] that propose a probabilistic framework for answer selection for traditional question answering.In this paper, we adapt a regression framework which is based on Gradient Boosting [25,9]. We now present our ranking framework in more detail.…”

Section: Related Workmentioning

confidence: 99%

“…Answers. Based on the extracted features and preference data, we apply the regression-based gradient boosting framework [25] to the problem of learning ranking function for QA retrieval.…”

Section: Learning Ranking Functions For Qa Retrievalmentioning

confidence: 99%

“…We use the approach in [25] and apply likelihood ratio test to examine whether a pair of answers is significant or not, i.e., whether there are enough votes to compare the pair. In , where s is positive constant, i.e., if the former value is bigger than the later one, then we say ans1 is preferred over ans2 which is denoted by ans1 ans2, and vice versa.…”

Section: Representing Users Evaluations As Preference Datamentioning

confidence: 99%

See 2 more Smart Citations

Emergency-relief coordination on social media: Automatically matching resource requests and offers

et al. 2013

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Community Question Answering has emerged as a popular and effective paradigm for a wide range of information needs. For example, to find out an obscure piece of trivia, it is now possible and even very effective to post a question on a popular community QA site such as Yahoo! Answers, and to rely on other users to provide answers, often within minutes. The importance of such community QA sites is magnified as they create archives of millions of questions and hundreds of millions of answers, many of which are invaluable for the information needs of other searchers. However, to make this immense body of knowledge accessible, effective answer retrieval is required. In particular, as any user can contribute an answer to a question, the majority of the content reflects personal, often unsubstantiated opinions. A ranking that combines both relevance and quality is required to make such archives usable for factual information retrieval. This task is challenging, as the structure and the contents of community QA archives differ significantly from the web setting. To address this problem we present a general ranking framework for factual information retrieval from social media. Results of a large scale evaluation demonstrate that our method is highly effective at retrieving well-formed, factual answers to questions, as evaluated on a standard factoid QA benchmark. We also show that our learning framework can be tuned with the minimum of manual labeling. Finally, we provide result analysis to gain deeper understanding of which features are significant for social media search and retrieval. Our system can be used as a crucial building block for combining results from a variety of social media content with general web search results, and to better integrate social media content for effective information access.

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Section: Related Workmentioning

confidence: 99%

“…Answers. Based on the extracted features and preference data, we apply the regression-based gradient boosting framework [25] to the problem of learning ranking function for QA retrieval.…”

Section: Learning Ranking Functions For Qa Retrievalmentioning

confidence: 99%

Section: Representing Users Evaluations As Preference Datamentioning

confidence: 99%

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Emergency-relief coordination on social media: Automatically matching resource requests and offers

et al. 2013

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“…These learning-to-rank approaches are capable of combining different kinds of features to train ranking functions. Especially, pair-wise learning-to-rank approaches, including RankSVM [8], RankNet [9], RankBoost [10], and GBRank [11], have become very popular. They learn the ranking function from pair-wise preference data by minimizing the number of contradicting pairs in training data.…”

Section: Related Workmentioning

confidence: 99%

Learning to blend vitality rankings from heterogeneous social networks

Bian

Chang

et al. 2012

Neurocomputing

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Heterogeneous social network services, such as Facebook and Twitter, have emerged as popular, and often effective channels for Web users to capture updates from their friends. The explosion in popularity of these social network services, however, has created the problem of "information overload". The problem is becoming more severe as more and more users have engaged in more than one social networks simultaneously, each of which usually yields different friend connections and various sources of updates. Thus, it has made necessity to perform effective information filtering to retrieve information really attractive to web users from each of social networks and further blend them into a unified ranking list. In this paper, we introduce the problem of blending vitality rankings from heterogeneous social networks, where vitality denotes all kinds of updates user receives in various social networks. We propose a variety of content, users, and users correlation features for this task. Since vitalities from different social networks are likely to have different sets of features, we employ a divide-and-conquer strategy in order to fully exploit all available features for vitalities from each social network, respectively. Our experimental results, obtained from a large scale evaluation over two popular social networks, demonstrate the effectiveness of our method for putting vitalities that really interest users into higher orders in the blended ranking list. We complement our results with a thorough investigation of the feature importance and model selection with respect to both blending strategy and ranking for each social network.

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“…Pointwise methods, for example, decision tree models and linear regression, directly learn the relevance score of each instance; pairwise methods like rankSVM (Herbrich et al, 2000) learn to classify preference pairs; listwise methods such as LambdaMART (Burges, 2010) try to optimize the measurement for evaluating the whole ranking list. Some methods lie between two categories, for example, GBRank (Zheng et al, 2007) combines pointwise decision tree models and pairwise loss. Among them, rankSVM, as a pairwise approach, is one commonly used method.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Linear RankSVM

Lee

2014

Neural Computation

105

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Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following the recent development of linear SVM for classification, linear rankSVM may give competitive performance for large and sparse data. Many existing works have studied linear rankSVM. Their focus is on the computational efficiency when the number of preference pairs is large. In this paper, we systematically study past works, discuss their advantages/disadvantages, and propose an efficient algorithm. Different implementation issues and extensions are discussed with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.

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A regression framework for learning ranking functions using relative relevance judgments

Cited by 162 publications

References 21 publications

Emergency-relief coordination on social media: Automatically matching resource requests and offers

Emergency-relief coordination on social media: Automatically matching resource requests and offers

Learning to blend vitality rankings from heterogeneous social networks

Large-Scale Linear RankSVM

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