Learning to Rank: Online Learning, Statistical Theory and Applications.

Learning to rank is a supervised machine learning problem, where the output space is the special structured space of emph{permutations}. Learning to rank has diverse application areas, spanning information retrieval, recommendation systems, computational biology and others. In this dissertation, we make contributions to some of the exciting directions of research in learning to rank. In the first part, we extend the classic, online perceptron algorithm for classification to learning to rank, giving a loss bound which is reminiscent of Novikoff's famous convergence theorem for classification. In the second part, we give strategies for learning ranking functions in an online setting, with a novel, feedback model, where feedback is restricted to labels of top ranked items. The second part of our work is divided into two sub-parts; one without side information and one with side information. In the third part, we provide novel generalization error bounds for algorithms applied to various Lipschitz and/or smooth ranking surrogates. In the last part, we apply ranking losses to learn policies for personalized advertisement recommendations, partially overcoming the problem of click sparsity. We conduct experiments on various simulated and commercial datasets, comparing our strategies with baseline strategies for online learning to rank and personalized advertisement recommendation.PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133334/1/sougata_1.pd

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.Siirretty Doriast

Generalization Analysis of Listwise Learning-to-Rank Algorithms

This paper presents a theoretical framework for ranking, and demonstrates how to perform generalization analysis of listwise ranking algorithms using the framework. Many learning-to-rank algorithms have been proposed in recent years. Among them, the listwise approach has shown higher empirical ranking performance when compared to the other approaches. However, there is no theoretical study on the listwise approach as far as we know. In this paper, we propose a theoretical framework for ranking, which can naturally describe various listwise learningto-rank algorithms. With this framework, we prove a theorem which gives a generalization bound of a listwise ranking algorithm, on the basis of Rademacher Average of the class of compound functions. The compound functions take listwise loss functions as outer functions and ranking models as inner functions. We then compute the Rademacher Averages for existing listwise algorithms of ListMLE, ListNet, and RankCosine. We also discuss the tightness of the bounds in different situations with regard to the list length and transformation function

Transfer learning for information retrieval

The lack of relevance labels is increasingly challenging and presents a bottleneck in the training of reliable learning-to-rank (L2R) models. Obtaining relevance labels using human judgment is expensive and even impossible in some scenarios. Previous research has studied different approaches to solving the problem including generating relevance labels by crowdsourcing and active learning. Recent studies have started to find ways to reuse knowledge from a related collection to help the ranking in a new collection. However, the effectiveness of a ranking function trained in one collection may be degraded when used in another collection due to the generalization issues of machine learning. Transfer learning involves a set of algorithms that are used to train or adapt a model for a target collection without sucient training labels by transferring knowledge from a related source collection with abundant labels. Transfer learning can also be applied to L2R to help train ranking functions for a new task by reusing data from a related collection while minimizing the generalization gap. Some attempts have been made to apply transfer learning techniques on L2R tasks. This thesis investigates different approaches to transfer learning methods for L2R, which are called transfer ranking. However, most of the existing studies on transfer ranking have been focused on the scenario when there are a small but not sucient number of relevance labels. The field of transfer ranking with no target collection labels is still relatively undeveloped. Moreover, the main reason why a transfer ranking solution is needed is that a ranking function trained in the source collection cannot generalize to the target collection, due to the differences in the data distribution of the two collections. However, the effect of the data distribution differences on ranking model generalization has not been examined in detail. The focus of this study is the scenario when there are no relevance labels from the new collection (the target collection), but where a related collection (the target collection) has an abundant amount of training data and labels. In this thesis, we first demonstrate the generalization gap of different L2R algorithms when the distribution of the source and target collections are different in multiple ways, and we then develop alternative solutions to tackling the problem, which includes instance weighting algorithms and self-labeling methods. Instance weighting algorithms estimate weights for each training query in the source collection according to the target query distribution and use the weighted objective function to optimize a ranking function for the target collection. The results on different test collections suggest that instance weighting methods, including existing approaches, are not reliable. The self-labeling methods use other approaches to generate imputed relevance labels for queries in the target collection, which look to transfer the ranking knowledge to the target collection by transferring the label knowledge. The algorithms were tested on various transferring scenarios and showed significant effectiveness and consistency. We thus demonstrate that the performance of self-labeling methods can be further improved with a minimal number of calibration labels from the target collection. The algorithms and knowledge developed in this thesis can help solve generic ranking knowledge transfer problems under different scenarios

Adquisición y representación del conocimiento mediante procesamiento del lenguaje natural

[Resumen] Este trabajo introduce un marco para la recuperación de información combinando el procesamiento del lenguaje natural y conocimiento de un dominio, abordando la totalidad del proceso de creación, gestión e interrogación de una colección documental. La perspectiva empleada integra automáticamente conocimiento lingüístico en un modelo formal de representación semántica, directamente manejable por el sistema. Ello permite la construcción de algoritmos que simplifican las tareas de mantenimiento, proporcionan un acceso más flexible al usuario no especializado, y eliminan componentes subjetivas que lleven a comportamientos difícilmente predecibles. La adquisición de conocimientos lingüísticos parte de un análisis de dependencias basado en un formalismo gramatical suavemente dependiente del contexto. Conjugamos de este modo eficacia computacional y potencia expresiva. La interpretación formal de la semántica descansa en la noción de grafo conceptual, sirviendo de base para la representación de la colección y para las consultas que la interrogan. En este contexto, la propuesta resuelve la generación automática de estas representaciones a partir del conocimiento lingüístico adquirido de los textos y constituyen el punto de partida para su indexación. Luego, se utilizan operaciones sobre grafos así como el principio de proyección y generalización para calcular y ordenar las respuestas, de tal manera que se considere la imprecisión intrínseca y el carácter incompleto de la recuperación. Además, el aspecto visual de los grafos permiten la construcción de interfaces de usuario amigables, conciliando precisión e intuición en su gestión. En este punto, la propuesta también engloba un marco de pruebas formales.[Resumo] Este traballo introduce un marco para a recuperación de información combinando procesamento da linguaxe natural e o coñecemento dun dominio, abordando a totalidade do proceso de creación, xestión e interrogación dunha colección documental. A perspectiva empregada integra automáticamente coñecementos lingüísticos nun modelo formal de representación semántica, directamente manexable polo sistema. Isto permite a construción de algoritmos que simplifican as tarefas de mantemento, proporcionan un acceso máis flexible ao usuario non especializado, e eliminan compoñentes subxectivos que levan a comportamentos difícilmente predicibles. A adquisición de coñecementos lingüísticos parte duhna análise de dependencias basada nun formalismo gramatical suavemente dependente do contexto. Conxugamos deste modo eficacia computacional e potencia expresiva. A interpretación formal da semántica descansa na noción de grafo conceptual, servindo de base para a representación da colección e para as consultas que a interrogan. Neste contexto, a proposta resolve a xeración automática destas representacións a partires do coñecemento lingüístico adquirido dos textos e constitúe o punto de partida para a súa indexación. Logo, empréganse operacións sobre grafos así como o principio de proxección e xeneralización para calcular e ordenar as respostas, de tal maneira que se considere a imprecisión intrínseca e o carácter incompleto da recuperación. Ademáis, o aspecto visual dos grafos permiten a construción de interfaces de usuario amigables, conciliando precisión e intuición na súa xestión. Neste punto, a proposta tamén engloba un marco de probas formais.[Abstract] This thesis introduces a framework for information retrieval combining natural language processing and a domain knowledge, dealing with the whole process of creation, management and interrogation of a documental collection. The perspective used integrates automatically linguistic knowledge in a formal model of semantic representation directly manageable by the system. This allows the construction of algorithms that simplify maintenance tasks, provide more flexible access to non-specialist user, and eliminate subjective components that lead to hardly predictable behavior. The linguistic knowledge adquisition starts from a dependency parse based on a midly context-sensitive grammatical formalism. In this way, we combine computational efficiency and expressive power. The formal interpretation of the semantics is based on the notion of conceptual graph, providing a basis for the representation of the collection and for queries that interrogate. In this context, the proposal addresses the automatic generation of these representations from linguistic knowledge acquired from texts and constitute the starting point for indexing. Then operations on graphs are used and the principle of projection and generalization to calculate and manage replies, so that is considered the inherent inaccuracy and incompleteness of the recovery. In addition, the visual aspect of graphs allow the construction of user-friendly interfaces, balancing precision and intuition in management. At this point, the proposal also includes a framework for formal testing