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Ranking is the central problem for many applications such as web search, recommendation systems, and visual comparison of images. In this paper, the multiple kernel learning framework is generalized for the learning to rank problem. This... more
The PageRank algorithm is an iterative algorithm used in the Google search engine to improve the results of requests by taking into account the link structure of the web. More interesting and intelligent surfer model combining the link... more
The PageRank algorithm is an iterative algorithm used in the Google search engine to improve the results of requests by taking into account the link structure of the web. More interesting and intelligent surfer model combining the link... more
Resorting to community question answering (CQA) websites for finding answers has gained momentum in the past decade with the explosive rate at which social media has been proliferating. With many questions left unanswered on those... more
For massive and heterogeneous modern datasets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of rank aggregation, for the Plackett-Luce... more
For massive and heterogeneous modern datasets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of learning to rank, we provide a hierarchy of... more
For massive and heterogeneous modern datasets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of rank aggregation, for the Plackett-Luce... more
For massive and heterogeneous modern datasets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of learning to rank, we provide a hierarchy of... more
Emotion recognition in conversations (ERC) focuses on identifying emotion shifts within interactions, representing a significant step toward advancing machine intelligence. However, ERC data remains scarce, and existing datasets face... more
We demonstrate that there are machine learning algorithms that can achieve success for two separate tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that advantages gained from solving one task... more
Domain-specific languages that use a lot of specific terminology often fall into the category of low-resource languages. Collecting test datasets in a narrow domain is time-consuming and requires skilled human resources with domain... more
We consider the problem of prediction with expert advice when the losses of the experts have low-dimensional structure: they are restricted to an unknown d-dimensional subspace. We devise algorithms with regret bounds that are independent... more
We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building meta-search engines, combining ranking functions, selecting documents based on multiple criteria,... more
The task of retrieving information that really matters to the users is considered hard when taking into consideration the current and increasingly amount of available information. To improve the effectiveness of this information seeking... more
In this paper we present a semi-supervised learning method for a problem of learning to rank where we exploit Markov random walks and graph regularization in order to incorporate not only "labeled" web pages but also plenty of "unlabeled"... more
Learning to Rank (L2R) is the core task of many Information Retrieval systems. Recently, a great effort has been put on exploring Deep Neural Networks (DNNs) for L2R, with significant results. However, risk-sensitiveness, an important and... more
Learning to Rank (L2R) is one of the main research lines in Information Retrieval. Risk-sensitive L2R is a sub-area of L2R that tries to learn models that are good on average while at the same time reducing the risk of performing poorly... more
The task of determining labels of all network nodes based on the knowledge about network structure and labels of some training subset of nodes is called the within-network classification. It may happen that none of the labels of the nodes... more
This paper demonstrates how image content can be used to realize a location-based shopping recommender system for intuitively supporting mobile users in decision making. Generic Fourier Descriptors (GFD) image content of an item was... more
We consider an online decision problem over a discrete space in which the loss function is submodular. We give algorithms which are computationally efficient and are Hannan-consistent in both the full information and bandit settings.
Relevance ranking algorithms rank retrieved documents based on the degrees of topical similarity (relevance) between search queries and documents. This paper aims to introduce a new relevance ranking method combining a probabilistic topic... more
Learning to rank in Information Retrieval is the problem of learning the full order of a set of documents from their partially observed order. Datasets used by learning to rank algorithms are growing enormously in terms of number of... more
Search engines are very useful tool now a days to fulfill the information need of a user. The performance of search engine mainly depends on page ranking algorithm which provides highly relevant web pages at the top of the search result.
Ranking thousands of web documents so that they are matched in response to a user query is really a challenging task. For this purpose, search engines use different ranking mechanisms on apparently related resultant web documents to... more
Information searching is the most popular activity in Internet. Usually the search engine provides the search results ranked by the relevance. However, for a certain purpose that concerns with information credibility, particularly citing... more
Modern large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified and ranked first for presentation to... more
Online learning to rank (OLTR) aims to learn a ranker directly from implicit feedback derived from users’ interactions, such as clicks. Clicks however are a biased signal: specifically, top-ranked documents are likely to attract more... more
Microblogging sites are important vehicles for the users to obtain information and shape public opinion thus they are arenas of continuous competition for popularity. Most popular topics are usually indicated on ranking lists. In this... more
Ranking is the central problem for many applications such as web search, recommendation systems, and visual comparison of images. In this paper, the multiple kernel learning framework is generalized for the learning to rank problem. This... more
This paper proposes the idea of ranking definitions of a person (a set of biographical facts) to automatically generate "Who is this?" quizzes. The definitions are ordered according to how difficult they make it to name the person. Such... more
To improve the accuracy of recommendations, alleviate sparse data problems, and mitigate the homogenization of traditional socialized recommendations, a gated recurrent neural network is studied to construct a relevant user preference... more
Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data-hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.). The goal of active learning is to... more
Originally formulated in Social Choice theory, Ranking Aggregation, also referred to as Consensus Ranking, has motivated the development of numerous statistical models since the middle of the 20th century. Recently, the analysis of... more
Nowadays, it's a very significant way for researchers and other individuals to achieve their interests because it provides short solutions to satisfy their demands. Because there are so many pieces of information on the internet, news... more
We present a novel framework that applies a metalearning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dataset. This ranking... more
The type of an entity is a key piece of information to understand what an entity is and how it relates to other entities mentioned in a document. Search engine result pages (SERPs) often surface facts and entity type information from a... more
The Learning to Rank (L2R) research field has experienced a fast paced growth over the last few years, with a wide variety of benchmark datasets and baselines available for experimentation. We here investigate the main assumption behind... more
In recent years, deep learning-based algorithms such as CNN, LSTM, and auto-encoders have been proposed to rank suspicious buggy őles. Meanwhile, representational learning has served to be the best approach to extract rich semantic... more
Pseudo test collections are automatically generated to provide training material for learning to rank methods. We propose a method for generating pseudo test collections in the domain of digital libraries, where data is relatively sparse,... more
The World Wide Web contains the large amount of information sources and these are increasing tremendously. When the user searching the web for information retrieval, user may fetch irrelevant and redundant data causing a waste in user... more
Counterfactual evaluation plays a crucial role in learning-to-rank problems, as it addresses the discrepancy between the data logging policy and the policy being evaluated, due to the presence of presentation bias. Existing counterfactual... more
Web archives already hold together more than 534 billion files and this number continues to grow as new initiatives arise. Searching on all versions of these files acquired throughout time is challenging, since users expect as fast and... more
Learning to rank is a technique in machine learning for ranking problem. This paper aims to investigate this technique to classify the responsible agencies of each complaint text of LAPOR, which is our government complaint management... more
We present a novel method for ranking query paraphrases for effective search in community question answering (cQA). The method uses query logs from Yahoo! Search and Yahoo! Answers for automatically extracting a corpus of paraphrases of... more
Platforms for community-based Question Answering (cQA) are playing an increasing role in the synergy of informationseeking and social networks. Being able to categorize user questions is very important, since these categories are good... more
Explicitly modelling field interactions and correlations in complex document structures has recently gained popularity in neural document embedding and retrieval tasks. Although this requires the specification of bespoke task-dependent... more
It is the purpose of this paper to formulate the issue of scoring multivariate observations depending on their degree of abnormality/novelty as an unsupervised learning task. Whereas in the 1-d situation, this problem can be dealt with by... more
This paper is devoted to thoroughly investigating how to bootstrap the ROC curve, a widely used visual tool for evaluating the accuracy of test/scoring statistics in the bipartite setup. The issue of confidence bands for the ROC curve is... more
We formulate a local form of the bipartite ranking problem where the goal is to focus on the best instances. We propose a methodology based on the construction of real-valued scoring functions. We study empirical risk minimization of... more