This paper considers the problem of document ranking in information retrieval systems by Learning to Rank. We propose ConvRankNet combining a Siamese Convolutional Neural Network encoder and the RankNet ranking model which could be trained in an end-to-end fashion. We prove a general result justifying the linear test-time complexity of pairwise Learning to Rank approach. Experiments on the OHSUMED dataset show that ConvRankNet outperforms systematically existing feature-based models.
Pairwise approach is extensively studied under supervised setting. As its name suggests, it takes a pair of documents @math and @math as input and is trained to predict if @math is more relevant than @math . @cite_7 uses clickthough log'' to infer pairwise preference and trains a linear Support Vector Machines (SVM) on the difference of feature vectors @math . @cite_0 introduce a three-layer Siamese neural network with a probabilistic cost function which can be efficiently optimized by gradient descent. Instead of working with non-smooth cost function, @cite_2 propose LambdaRank which model directly the gradient of an implicit cost function. @cite_1 introduces LambdaMART which is the boosted tree version of LambdaRank. LambdaMART is generally considered as the state-of-the-art supervised ranking model.
@cite_0 @cite_1 @cite_7 @cite_2
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State-of-the-art item recommendation algorithms, which apply Factorization Machines (FM) as a scoring function and pairwise ranking loss as a trainer (PRFM for short), have been recently investigated for the implicit feedback based context-aware recommendation problem (IFCAR). However, good recommenders particularly emphasize on the accuracy near the top of the ranked list, and typical pairwise loss functions might not match well with such a requirement. In this paper, we demonstrate, both theoretically and empirically, PRFM models usually lead to non-optimal item recommendation results due to such a mismatch. Inspired by the success of LambdaRank, we introduce Lambda Factorization Machines (LambdaFM), which is particularly intended for optimizing ranking performance for IFCAR. We also point out that the original lambda function suffers from the issue of expensive computational complexity in such settings due to a large amount of unobserved feedback. Hence, instead of directly adopting the original lambda strategy, we create three effective lambda surrogates by conducting a theoretical analysis for lambda from the top-N optimization perspective. Further, we prove that the proposed lambda surrogates are generic and applicable to a large set of pairwise ranking loss functions. Experimental results demonstrate LambdaFM significantly outperforms state-of-the-art algorithms on three real-world datasets in terms of four standard ranking measures. 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. Pairwise algorithms are popular for learning recommender systems from implicit feedback. For each user, or more generally context, they try to discriminate between a small set of selected items and the large set of remaining (irrelevant) items. Learning is typically based on stochastic gradient descent (SGD) with uniformly drawn pairs. In this work, we show that convergence of such SGD learning algorithms slows down considerably if the item popularity has a tailed distribution. We propose a non-uniform item sampler to overcome this problem. The proposed sampler is context-dependent and oversamples informative pairs to speed up convergence. An efficient implementation with constant amortized runtime costs is developed. Furthermore, it is shown how the proposed learning algorithm can be applied to a large class of recommender models. The properties of the new learning algorithm are studied empirically on two real-world recommender system problems. The experiments indicate that the proposed adaptive sampler improves the state-of-the art learning algorithm largely in convergence without negative effects on prediction quality or iteration runtime. Recently, there has been a renewed interest in the machine learning community for variants of a sparse greedy approximation procedure for concave optimization known as the Frank-Wolfe (FW) method. In particular, this procedure has been successfully applied to train large-scale instances of non-linear Support Vector Machines (SVMs). Specializing FW to SVM training has allowed to obtain efficient algorithms, but also important theoretical results, including convergence analysis of training algorithms and new characterizations of model sparsity.In this paper, we present and analyze a novel variant of the FW method based on a new way to perform away steps, a classic strategy used to accelerate the convergence of the basic FW procedure. Our formulation and analysis is focused on a general concave maximization problem on the simplex. However, the specialization of our algorithm to quadratic forms is strongly related to some classic methods in computational geometry, namely the Gilbert and MDM algorithms.On the theoretical side, we demonstrate that the method matches the guarantees in terms of convergence rate and number of iterations obtained by using classic away steps. In particular, the method enjoys a linear rate of convergence, a result that has been recently proved for MDM on quadratic forms.On the practical side, we provide experiments on several classification datasets, and evaluate the results using statistical tests. Experiments show that our method is faster than the FW method with classic away steps, and works well even in the cases in which classic away steps slow down the algorithm. Furthermore, these improvements are obtained without sacrificing the predictive accuracy of the obtained SVM model.