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Adapting ranking SVM to document retrieval

Adapting ranking SVM to document retrieval,10.1145/1148170.1148205,Yunbo Cao,Jun Xu,Tie-yan Liu,Hang Li,Yalou Huang,Hsiao-wuen Hon

Adapting ranking SVM to document retrieval   (Citations: 121)
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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.
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    • ...Learningto-rank methods (e.g., [6, 8, 9, 15, 17, 47]) have been proposed to optimize a ranking function that incorporates a variety of features and avoids tuning a large number of parameters empirically...
    • ...One of the problems with these approaches is that the training model might be biased towards queries with more document pairs [8]...

    Maryam Karimzadehganet al. A stochastic learning-to-rank algorithm and its application to context...

    • ...Evaluation measure:There are several measures for evaluating ranking such as AUC (Area Under the Curve), MAP (Mean Average Precision), NDCG, and Kendall’s τ. AUC or MAP is used when ranking is binary (e.g., relevant or non-relevant), and NDCG is used when ranking is a multiple levels of relevance judgement (Cao et al. 2006; Burges et al. 2004; Qin et al. 2007; Xu and Li 2007)...

    Hwanjo Yu. Selective sampling techniques for feedback-based data retrieval

    • ...A comparison with other ranking algorithms (RankSupport Vector Machines (SVM) [8], RankBoost [9], FRank [10], ListNet [11], and AdaRank [12]), carried out using several performance measures, reports promising results...
    • ...In [8], the authors propose a pairwise learning approach to train a SVM model (SVMRank), while AdaRank [12] uses an AdaBoost scheme to learn the preference function...
    • ...The SortNet algorithm has been compared on ranking tasks with other methods, i.e., RankSVM [8], RankBoost [9], FRank [10], ListNet [11], AdaRank MAP and AdaRank NDCG [12], using the measures proposed for the LETOR benchmark [7] (see Section IV-B): P@n, MAP, NDCG@n...

    Leonardo Rigutiniet al. SortNet: Learning to Rank by a Neural Preference Function

    • ...The applications of learning to rank include document retrieval [1], entity search and so on. Several methods have been proposed...

    Yang Wanget al. Multiple query-dependent RankSVM aggregation for document retrieval

    • ...Ranking support vector machine (SVM) [1] is the most favorite ranking method that was applied to various different applications [2], [3], [4]...
    • ...And assume that we are given a ranking dataset (detailed in [6], [1], [4])...

    Nguyen Thi Thanh Thuyet al. Nomogram Visualization for Ranking Support Vector Machine

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