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34
Statistical ranking and combinatorial Hodge theory
 Mathematical Programming
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Generalization Bounds for Ranking Algorithms via Algorithmic Stability
 J. of Machine Learning Research
"... The problem of ranking, in which the goal is to learn a realvalued ranking function that induces a ranking or ordering over an instance space, has recently gained much attention in machine learning. We study generalization properties of ranking algorithms using the notion of algorithmic stability; ..."
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The problem of ranking, in which the goal is to learn a realvalued ranking function that induces a ranking or ordering over an instance space, has recently gained much attention in machine learning. We study generalization properties of ranking algorithms using the notion of algorithmic stability; in particular, we derive generalization bounds for ranking algorithms that have good stability properties. We show that kernelbased ranking algorithms that perform regularization in a reproducing kernel Hilbert space have such stability properties, and therefore our bounds can be applied to these algorithms; this is in contrast with generalization bounds based on uniform convergence, which in many cases cannot be applied to these algorithms. Our results generalize earlier results that were derived in the special setting of bipartite ranking (Agarwal and Niyogi, 2005) to a more general setting of the ranking problem that arises frequently in applications.
An Efficient Reduction of Ranking to Classification
, 2007
"... This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction is randomized and guarantees a pairwise misranking regret bounded by that of the binary classifier, improving on a recent result of Balcan et al. (2007) which ensures only twice tha ..."
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This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction is randomized and guarantees a pairwise misranking regret bounded by that of the binary classifier, improving on a recent result of Balcan et al. (2007) which ensures only twice that upperbound. Moreover, our reduction applies to a broader class of ranking loss functions, admits a simple proof, and the expected time complexity of our algorithm in terms of number of calls to a classifier or preference function is also improved from Ω(n 2) to O(n log n). In addition, when the top k ranked elements only are required (k ≪ n), as in many applications in information extraction or search engine design, the time complexity of our algorithm can be further reduced to O(k log k+n). Our reduction and algorithm are thus practical for realistic applications where the number of points to rank exceeds several thousands. Much of our results also extend beyond the bipartite case previously studied. To further complement them, we also derive lower bounds for any deterministic reduction of ranking to binary classification, proving that randomization is necessary to achieve our reduction guarantees. 1
An alternative ranking problem for search engines
 in Proc. WEA07, LNCS 4525, 2007
"... Abstract. This paper examines in detail an alternative ranking problem for search engines, movie recommendation, and other similar ranking systems motivated by the requirement to not just accurately predict pairwise ordering but also preserve the magnitude of the preferences or the difference betw ..."
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Abstract. This paper examines in detail an alternative ranking problem for search engines, movie recommendation, and other similar ranking systems motivated by the requirement to not just accurately predict pairwise ordering but also preserve the magnitude of the preferences or the difference between ratings. We describe and analyze several cost functions for this learning problem and give stability bounds for their generalization error, extending previously known stability results to nonbipartite ranking and magnitude of preferencepreserving algorithms. We present algorithms optimizing these cost functions, and, in one instance, detail both a batch and an online version. For this algorithm, we also show how the leaveoneout error can be computed and approximated efficiently, which can be used to determine the optimal values of the tradeoff parameter in the cost function. We report the results of experiments comparing these algorithms on several datasets and contrast them with those obtained using an AUCmaximization algorithm. We also compare training times and performance results for the online and batch versions, demonstrating that our online algorithm scales to relatively large datasets with no significant loss in accuracy. 1
Preferencebased learning to rank
 MACHINE LEARNING
, 2010
"... This paper presents an efficient preferencebased ranking algorithm running in two stages. In the first stage, the algorithm learns a preference function defined over pairs, as in a standard binary classification problem. In the second stage, it makes use of that preference function to produce an ac ..."
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Cited by 4 (1 self)
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This paper presents an efficient preferencebased ranking algorithm running in two stages. In the first stage, the algorithm learns a preference function defined over pairs, as in a standard binary classification problem. In the second stage, it makes use of that preference function to produce an accurate ranking, thereby reducing the learning problem of ranking to binary classification. This reduction is based on the familiar QuickSort and guarantees an expected pairwise misranking loss of at most twice that of the binary classifier derived in the first stage. Furthermore, in the important special case of bipartite ranking, the factor of two in loss is reduced to one. This improved bound also applies to the regret achieved by our ranking and that of the binary classifier obtained. Our algorithm is randomized, but we prove a lower bound for any deterministic reduction of ranking to binary classification showing that randomization is necessary to achieve our guarantees. This, and a recent result by Balcan et al., who show a regret bound of two for a deterministic algorithm in the bipartite case, suggest a tradeoff between achieving low regret and determinism in this context. Our reduction also admits an improved running time guarantee with respect to that deterministic algorithm. In particular, the number of calls to the preference function in the reduction is improved from Ω(n 2) to O(n log n). In addition, when the top k ranked elements only are required (k ≪n), as in many applications in information extraction or search engine design, the time complexity of our algorithm can be further reduced to O(k log k+n). Our algorithm is thus practical for realistic applications where the number of points to rank exceeds several thousand.
An Online Learning Framework for Refining Recency Search Results with User Click Feedback
"... Traditional machinelearned ranking systems for Web search are often trained to capture stationary relevance of documents to queries, which have limited ability to track nonstationary user intention in a timely manner. In recency search, for instance, the relevance of documents to a query on breakin ..."
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Traditional machinelearned ranking systems for Web search are often trained to capture stationary relevance of documents to queries, which have limited ability to track nonstationary user intention in a timely manner. In recency search, for instance, the relevance of documents to a query on breaking news often changes significantly over time, requiring effective adaptation to user intention. In this article, we focus on recency search and study a number of algorithms to improve ranking results by leveraging user click feedback. Our contributions are threefold. First, we use commercial search engine sessions collected in a random exploration bucket for reliable offline evaluation of these algorithms, which provides an unbiased comparison across algorithms without online bucket tests. Second, we propose an online learning approach that reranks and improves the search results for recency queries near realtime based on user clicks. This approach is very general and can be combined with sophisticated click models. Third, our empirical comparison of a dozen algorithms on realworld search data suggests importance of a few algorithmic choices in these applications, including generalization across different querydocument pairs, specialization to popular queries, and near realtime adaptation of user clicks for reranking.
A Sparse Regularized LeastSquares Preference Learning Algorithm
"... Abstract. Learning preferences between objects constitutes a challenging task that notably differs from standard classification or regression problems. The objective involves prediction of ordering of the data points. Furthermore, methods for learning preference relations usually are computationally ..."
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Abstract. Learning preferences between objects constitutes a challenging task that notably differs from standard classification or regression problems. The objective involves prediction of ordering of the data points. Furthermore, methods for learning preference relations usually are computationally more demanding than standard classification or regression methods. Recently, we have proposed a kernel based preference learning algorithm, called RankRLS, whose computational complexity is cubic with respect to the number of training examples. The algorithm is based on minimizing a regularized leastsquares approximation of a ranking error function that counts the number of incorrectly ranked pairs of data points. When nonlinear kernel functions are used, the training of the algorithm might be infeasible if the amount of examples is large. In this paper, we propose a sparse approximation of RankRLS whose training complexity is considerably lower than that of basic RankRLS. In our experiments, we consider parse ranking, a common problem in natural language processing. We show that sparse RankRLS significantly outperforms basic RankRLS in this task. To conclude, the advantage of sparse RankRLS is the computational efficiency when dealing with large amounts of training data together with high dimensional feature representations. 1.
LEARNING TO RANK WITH COMBINATORIAL HODGE THEORY
"... We propose a number of techniques for learning a global ranking from data that may be incomplete and imbalanced — characteristics that are almost universal to modern datasets coming from ecommerce and internet applications. We are primarily interested in cardinal data based on scores or ratings t ..."
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We propose a number of techniques for learning a global ranking from data that may be incomplete and imbalanced — characteristics that are almost universal to modern datasets coming from ecommerce and internet applications. We are primarily interested in cardinal data based on scores or ratings though our methods also give specific insights on ordinal data. From raw ranking data, we construct pairwise rankings, represented as edge flows on an appropriate graph. Our rank learning method exploits the graph Helmholtzian, which is the graph theoretic analogue of the Helmholtz operator or vector Laplacian, in much the same way the graph Laplacian is an analogue of the Laplace operator or scalar Laplacian. We shall study the graph Helmholtzian using combinatorial Hodge theory, which provides a way to unravel ranking information from edge flows. In particular, we show that every edge flow representing pairwise ranking can be resolved into two orthogonal components, a gradient flow that represents the l2optimal global ranking and a divergencefree flow (cyclic) that measures the validity of the global ranking
Learning to rank on graphs
 ICML
, 2010
"... Graph representations of data are increasingly common. Such representations arise in a variety of applications, including computational biology, social network analysis, web applications, and many others. There has been much work in recent years on developing learning algorithms for such graph data ..."
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Graph representations of data are increasingly common. Such representations arise in a variety of applications, including computational biology, social network analysis, web applications, and many others. There has been much work in recent years on developing learning algorithms for such graph data; in particular, graph learning algorithms have been developed for both classification and regression on graphs. Here we consider graph learning problems in which the goal is not to predict labels of objects in a graph, but rather to rank the objects relative to one another; for example, one may want to rank genes in a biological network by relevance to a disease, or customers in a social network by their likelihood of being interested in a certain product. We develop algorithms for such problems of learning to rank on graphs. Our algorithms build on the graph regularization ideas developed in the context of other graph learning problems, and learn a ranking function in a reproducing kernel Hilbert space (RKHS) derived from the graph. This allows us to show attractive stability and generalization properties. Experiments on several graph ranking tasks in computational biology and in cheminformatics demonstrate the benefits of our framework.