Learning to rank is utilized in many scenarios, such as disease-gene association, information retrieval and recommender system. Improving the prediction accuracy of the ranking model is the main target of existing works. Contextual information has a significant influence in the ranking problem, and has been proved effective to increase the prediction performance of ranking models. Then we construct similarities for different types of entities that could utilize contextual information uniformly in an extensible way. Once we have the similarities constructed by contextual information, how to uti- lize them for different types of ranking models will be the task we should tackle. In this thesis, we propose four algorithms for learning to rank with contextual informa- tion. To refine the framework of matrix factorization, we propose an area under the ROC curve (AUC) loss to conquer the sparsity problem. Clustering and sampling methods are used to utilize the contextual information in the global perspective, and an objective function with the optimal solution is proposed to exploit the contex- tual information in the local perspective. Then, for the deep learning framework, we apply the graph convolutional network (GCN) on the ranking problem with the combination of matrix factorization. Contextual information is utilized to generate the input embeddings and graph kernels for the GCN. The third method in this thesis is proposed to directly exploit the contextual information for ranking. Laplacian loss is utilized to solve the ranking problem, which could optimize the ranking matrix directly. With this loss, entities with similar contextual information will have similar ranking results. Finally, we propose a two-step method to solve the ranking problem of the sequential data. The first step in this two-step method is to generate the em- beddings for all entities with a new sampling strategy. Graph neural network (GNN) and long short-term memory (LSTM) are combined to generate the representation of sequential data. Once we have the representation of the sequential data, we could solve the ranking problem of them with pair-wise loss and sampling strategy.

Living in the booming age of information, we have to rely on powerful information retrieval tools to seek the unique piece of desired knowledge from such a big data world, like using personalized search engine and recommendation systems. As one of the core components, ranking model can appear in almost everywhere as long as we need a relative order of desired/relevant entities. Based on the most general and intuitive assumption that entities without user actions (e.g., clicks, purchase, comments) are of less interest than those with user actions, the objective function of pairwise ranking models is formulated by measuring the contrast between positive (with actions) and negative (without actions) entities. This contrastive relationship is the core of pairwise ranking models. The construction of these positive-negative pairs has great influence on the model inference accuracy. Especially, it is challenging to explore the entity relationships in heterogeneous information network. In this thesis, we aim at advancing the development of the methodologies and principles of mining heterogeneous information network through learning entity relations from a pairwise learning to rank optimization perspective. More specifically we first show the connections of different relation learning objectives modified from different ranking metrics including both pairwise and list-wise objectives. We prove that most of popular ranking metrics can be optimized in the same lower bound. Secondly, we propose the class-imbalance problem imposed by entity relation comparison in ranking objectives, and prove that class-imbalance problem can lead to frequency 5 clustering and gradient vanishment problems. As a response, we indicate out that developing a fast adaptive sampling method is very essential to boost the pairwise ranking model. To model the entity dynamic dependency, we propose to unify the individual-level interaction and union-level interactions, and result in a multi-order attentive ranking model to improve the preference inference from multiple views.

The main goal of this thesis is to investigate query-dependent selection of retrieval alternatives for Information Retrieval (IR) systems. Retrieval alternatives include choices in representing queries (query representations), and choices in methods used for scoring documents. For example, an IR system can represent a user query without any modification, automatically expand it to include more terms, or reduce it by dropping some terms. The main motivation for this work is that no single query representation or retrieval model performs the best for all queries. This suggests that selecting the best representation or retrieval model for each query can yield improved performance. The key research question in selecting between alternatives is how to estimate the performance of the different alternatives. We treat query dependent selection as a general problem of selecting between the result sets of different alternatives. We develop a relative effectiveness estimation technique using retrieval-based features and a learning formulation that directly predict differences between the results sets. The main idea behind this technique is to aggregate the scores and features used for retrieval (retrieval-based features) as evidence towards the effectiveness of the results set. We apply this general technique to select between alternatives reduced versions for long queries and to combine multiple ranking algorithms. Then, we investigate the extension of query-dependent selection under specific efficiency constraints. Specifically, we consider the black-box meta-search scenario, where querying all available search engines can be expensive and the features and scores used by the search engines are not available. We develop easy-to-compute features based on the results page alone to predict when querying an alternate search engine can be useful. Finally, we present an analysis of selection performance to better understand when query-dependent selection can be useful.

No description available.

No description available.

This dissertation represents a product of research conducted in 2004-2005 examining the curriculum network of an elementary teacher education program at a large public university in the United States. Using ethnographic data (e.g., interviews with preservice teachers and faculty, observations in and outside of coursework, and other artifacts), I address the questions of how preservice teachers characterized relationships between teacher education program components, how those characterizations varied and changed, and how preservice teachers explained the value or relevance of program components to teaching. I discuss how preservice teachers shaped their understandings of main program emphases. I describe how they tended to experience closer correspondence between program recommendations and the policies and philosophies in certain schools and classrooms in suburban county schools near the university compared to the policies and philosophies in certain schools and classrooms they identified as having, for example, fewer resources (e.g., funds, manipulatives). I make the case that the program-based philosophies developed by and for the preservice teachers helped to coordinate context-specific meanings and relevance for program components and further to construct failures of the kind where either (1) schools interfered with the accomplishment of program objectives or (2) program objectives proved unrealistic for schools. Without intending to, and perhaps even contrary to certain program intentions, program suggestions treating instruction as context-independent tended to favor middle-class White children and to marginalize urban or diverse schools and classrooms, or schools having more limited resources, as viable places to engage in program-recommended practices for good teaching. These results have potential implications for practice in teacher education and mathematics education and also have relevance to discussions of ongoing standards-based teacher education and mathematics education reforms. I offer that these results help to reveal certain limitations of popular ways of defining and researching preservice teachers' learning and teacher education program coursework and fieldwork relationships. I raise the question of whether teacher educators or researchers might benefit from considering how to more substantively integrate curriculum and give greater attention to place and to the broader socio-political goals we aim to accomplish through our work. / Ph. D.

The rapid growth of digital data, especially visual and textual contents, brings many challenges to the problem of finding similar data. Exact similarity search, which aims to exhaustively find all relevant items through a linear scan in a dataset, is impractical due to its high computational complexity. Approximate-nearest-neighbor (ANN) search methods, especially the Learning-to-hash or Hashing methods, provide principled approaches that balance the trade-offs between the quality of the guesses and the computational cost for web-scale databases. In this era of data explosion, it is crucial for the hashing methods to be both computationally efficient and robust to various scenarios such as when the application has noisy data or data that slightly changes over time (i.e., out-of-distribution). This Thesis focuses on the development of practical generative learning-to-hash methods and explainable retrieval models. We first identify and discuss the various aspects where the framework of generative modeling can be used to improve the model designs and generalization of the hashing methods. Then we show that these generative hashing methods similarly enjoy several appealing empirical and theoretical properties of generative modeling. Specifically, the proposed generative hashing models generalize better with important properties such as low-sample requirement, and out-of-distribution and data-corruption robustness. Finally, in domains with structured data such as graphs, we show that the computational methods in generative modeling have an interesting utility beyond estimating the data distribution and describe a retrieval framework that can explain its decision by borrowing the algorithmic ideas developed in these methods. Two subsets of generative hashing methods and a subset of explainable retrieval methods are proposed. For the first hashing subset, we propose a novel adversarial framework that can be easily adapted to a new problem domain and three training algorithms that learn the hash functions without several hyperparameters commonly found in the previous hashing methods. The contributions of our work include: (1) Propose novel algorithms, which are based on adversarial learning, to learn the hash functions; (2) Design computationally efficient Wasserstein-related adversarial approaches which have low computational and sample efficiency; (3) Conduct extensive experiments on several benchmark datasets in various domains, including computational advertising, and text and image retrieval, for performance evaluation. For the second hashing subset, we propose energy-based hashing solutions which can improve the generalization and robustness of existing hashing approaches. The contributions of our work for this task include: (1) Propose data-synthesis solutions to improve the generalization of existing hashing methods; (2) Propose energy-based hashing solutions which exhibit better robustness against out-of-distribution and corrupted data; (3) Conduct extensive experiments for performance evaluations on several benchmark datasets in the image retrieval domain. Finally, for the last subset of explainable retrieval methods, we propose an optimal alignment algorithm that achieves a better similarity approximation for a pair of structured objects, such as graphs, while capturing the alignment between the nodes of the graphs to explain the similarity calculation. The contributions of our work for this task include: (1) Propose a novel optimal alignment algorithm for comparing two sets of bag-of-vectors embeddings; (2) Propose a differentiable computation to learn the parameters of the proposed optimal alignment model; (3) Conduct extensive experiments, for performance evaluation of both the similarity approximation task and the retrieval task, on several benchmark graph datasets. / Doctor of Philosophy / Searching for similar items, or similarity search, is one of the fundamental tasks in this information age, especially when there is a rapid growth of visual and textual contents. For example, in a search engine such as Google, a user searches for images with similar content to a referenced image; in online advertising, an advertiser finds new users, and eventually targets these users with advertisements, where the new users have similar profiles to some referenced users who have previously responded positively to the same or similar advertisements; in the chemical domain, scientists search for proteins with a similar structure to a referenced protein. The practical search applications in these domains often face several challenges, especially when these datasets or databases can contain a large number (e.g., millions or even billions) of complex-structured items (e.g., texts, images, and graphs). These challenges can be organized into three central themes: search efficiency (the economical use of resources such as computation and time) and model-design effort (the ease of building the search model). Besides search efficiency and model-design effort, it is increasingly a requirement of a search model to possess the ability to explain the search results, especially in the scientific domains where the items are structured objects such as graphs. This dissertation tackles the aforementioned challenges in practical search applications by using the computational techniques that learn to generate data. First, we overcome the need to scan the entire large dataset for similar items by considering an approximate similarity search technique called hashing. Then, we propose an unsupervised hashing framework that learns the hash functions with simpler objective functions directly from raw data. The proposed retrieval framework can be easily adapted into new domains with significantly lower effort in model design. When labeled data is available but is limited (which is a common scenario in practical search applications), we propose a hashing network that can synthesize additional data to improve the hash function learning process. The learned model also exhibits significant robustness against data corruption and slight changes in the underlying data. Finally, in domains with structured data such as graphs, we propose a computation approach that can simultaneously estimate the similarity of structured objects, such as graphs, and capture the alignment between their substructures, e.g., nodes. The alignment mechanism can help explain the reason why two objects are similar or dissimilar. This is a useful tool for domain experts who not only want to search for similar items but also want to understand how the search model makes its predictions.

Information retrieval is widely used for retrieving relevant information among a variety of data, such as text documents, images, audio and videos. Since the first medical batch retrieval system was developed in mid 1960s, significant research efforts have focused on applying information retrieval to medical data. However, despite the vast developments in medical information retrieval and accompanying technologies, the actual promise of this area remains unfulfilled due to properties of medical data and the huge volume of medical literature. Specifically, the recall and precision of the selected dataset from the TREC clinical decision support track are low. The overriding objective of this thesis is to improve the performance of information retrieval techniques applied to biomedical text documents. We have focused on improving recall and precision among the top retrieved results. To that end, we have removed redundant words, and then expanded queries by adding MeSH terms in TREC CDS topics. We have also used other external data sources and domain knowledge to implement the expansion. In addition, we have also considered using the doc2vec model to optimize retrieval. Finally, we have applied learning to rank which sorts documents based on relevance and put relevant documents in front of irrelevant documents, so as to return the relevant retrieved data on the top. We have discovered that queries, expanded with external data sources and domain knowledge, perform better than applying the TREC topic information directly. / Master of Science / Information retrieval is widely used for retrieving relevant information among a variety of data. Since the first medical batch retrieval system was developed in mid 1960s, significant research efforts have focused on applying information retrieval to medical data. However the actual promise of this area remains unfulfilled due to certain properties of medical data and the sheer volume of medical literature. The overriding objective of this thesis is to improve the performance of information retrieval techniques applied to biomedical text documents. This thesis presents several ways to implement query expansion in order to make more efficient retrieval. Then this thesis discusses some approaches to put documents relevant to the queries at the top.

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-03-31T12:22:43Z No. of bitstreams: 2 Dissertação - Carina Calixto Ribeiro de Araujo - 2014.pdf: 962707 bytes, checksum: 35c8b1aaf03b3f0aeefb923de0f8dfcc (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-04-01T10:56:06Z (GMT) No. of bitstreams: 2 Dissertação - Carina Calixto Ribeiro de Araujo - 2014.pdf: 962707 bytes, checksum: 35c8b1aaf03b3f0aeefb923de0f8dfcc (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-04-01T10:56:06Z (GMT). No. of bitstreams: 2 Dissertação - Carina Calixto Ribeiro de Araujo - 2014.pdf: 962707 bytes, checksum: 35c8b1aaf03b3f0aeefb923de0f8dfcc (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-08-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Information Retrieval is an area of IT that deals with document storage and the information retrieval in these documents. With the advent of the Internet, the number of documents produced has increased as well as the need to retrieve the information more accurately. Many approaches have been proposed to meet these requirements and one of them is Learning to rank (L2R). Despite major advances achieved in the accuracy of retrived documents, there is still considerable room for improvement. This master thesis proposes the use of feature selection and generation using association rules to improve the accuracy of the L2R methods. / Recuperação de Informação é a área da informática que lida com o armazenamento de documentos e a recuperação de informação desses documentos. Com o advento da internet a quantidade de documentos produzidos aumentou, bem como a necessidade de recuperar a informação de forma mais mais precisa. Muitas abordagens surgiram para suprir essa requisição e uma delas é a abordagem Learning to Rank (L2R). Apesar de obtidos grandes avanços na precisão dos documentos retornados, ainda há espaço para melhorias. Esse trabalho de mestrado propõe a utilização de seleção e geração de características utilizando regras de associação para conseguir uma melhoria na acurácia dos métodos de L2R.

Syftet med denna studie är att undersöka läs- och skrivinlärningen i förskoleklass och årskurs 1. Studien undersöker när elevers läs- och skrivinlärning påbörjas och hur lång tid denna inlärning tar. Dessutom undersöker studien vilka metoder som lärarna använder sig av i sin undervisning för att utveckla elevers läs- och skrivförmåga. En enkät skickades ut för att samla data om hur verksamma lärare i förskoleklass och årskurs 1 arbetar med läs- och skrivinlärningen med hjälp av Läsning på talets grund, Whole language, Tragetonmetoden och Wittingmetoden. Lärarna fick även nämna övriga metoder de använde sig av i sin undervisning. De flesta lärare börjar med läs- och skrivinlärningen i sin undervisning direkt då de får sina elever. Denna läs- och skrivinlärning tar ungefär ett halvår för en elev utan större läs- och skrivsvårigheter. Allra vanligast är det att en lärare använder sig av en blandning av läs- och skrivinlärningsmetoder i sin undervisning. / The aim of this study is to investigate how educators in preschool-class and year 1 in primary school teach their pupils how to read and write, and what methods teachers use to aid this process. The aim is also to see what the connection is between the time it takes for the pupils to learn how to read and write and their development towards literacy. A questionnaire was sent out to collect data of how teachers in preschool-class and year 1 work with literacy using LTG, Whole language, the Wittingmethod and the Tragetonmethod. The teachers also got the opportunity to mention other methods that they used in their education. Most of the teachers immediately start teaching the pupils how to read and write. Learning how to read and write takes approximately half a year for a pupil without any reading and/or writing difficulties. Teachers commonly use a variety of methods in their education.