Topic
Ranking (information retrieval)
About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.
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24 Oct 2016TL;DR: This article proposed an attention-based neural matching model for ranking short answer text, which adopts value-shared weighting scheme instead of position shared weighting for combining different matching signals and incorporate question term importance learning using question attention network.
Abstract: As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers. To achieve good results, however, these models have been combined with additional features such as word overlap or BM25 scores. Without this combination, these models perform significantly worse than methods based on linguistic feature engineering. In this paper, we propose an attention based neural matching model for ranking short answer text. We adopt value-shared weighting scheme instead of position-shared weighting scheme for combining different matching signals and incorporate question term importance learning using question attention network. Using the popular benchmark TREC QA data, we show that the relatively simple aNMM model can significantly outperform other neural network models that have been used for the question answering task, and is competitive with models that are combined with additional features. When aNMM is combined with additional features, it outperforms all baselines.
200 citations
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03 Jan 2002TL;DR: In this paper, a search and a browse on a single user query is performed, and a refined query is selected from the results of the first user query by selecting concepts from a first directory associated with the refined query.
Abstract: A search and a browse on a single user query is performed. A refined query is selected from the results of the first user query. Thereafter, a list of concepts from a first directory associated with the refined query is obtained. The concepts are defined in a hierarchical relationship with concepts having broader scope being higher in the hierarchy and concepts having a narrower scope being lower in the hierarchy. Additionally, a list of web sites associated with the search concept is obtained from a second directory.
200 citations
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04 Feb 2013
TL;DR: This paper builds predictive models for user decisions in Twitter by proposing Co-Factorization Machines (CoFM), an extension of a state-of-the-art recommendation model, to handle multiple aspects of the dataset at the same time, and concludes that CoFM with ranking-based loss functions is superior to state of theart methods and yields interpretable latent factors.
Abstract: Users of popular services like Twitter and Facebook are often simultaneously overwhelmed with the amount of information delivered via their social connections and miss out on much content that they might have liked to see, even though it was distributed outside of their social circle. Both issues serve as difficulties to the users and drawbacks to the services.Social media service providers can benefit from understanding user interests and how they interact with the service, potentially predicting their behaviors in the future. In this paper, we address the problem of simultaneously predicting user decisions and modeling users' interests in social media by analyzing rich information gathered from Twitter. The task differs from conventional recommender systems as the cold-start problem is ubiquitous, and rich features, including textual content, need to be considered. We build predictive models for user decisions in Twitter by proposing Co-Factorization Machines (CoFM), an extension of a state-of-the-art recommendation model, to handle multiple aspects of the dataset at the same time. Additionally, we discuss and compare ranking-based loss functions in the context of recommender systems, providing the first view of how they vary from each other and perform in real tasks. We explore an extensive set of features and conduct experiments on a real-world dataset, concluding that CoFM with ranking-based loss functions is superior to state-of-the-art methods and yields interpretable latent factors.
199 citations
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IBM1
TL;DR: This work presents a vocabulary independent system that can handle arbitrary queries, exploiting the information provided by having both word transcripts and phonetic transcripts, in order to retrieve information from speech data.
Abstract: We are interested in retrieving information from speech data like broadcast news, telephone conversations and roundtable meetings. Today, most systems use large vocabulary continuous speech recognition tools to produce word transcripts; the transcripts are indexed and query terms are retrieved from the index. However, query terms that are not part of the recognizer's vocabulary cannot be retrieved, and the recall of the search is affected. In addition to the output word transcript, advanced systems provide also phonetic transcripts, against which query terms can be matched phonetically. Such phonetic transcripts suffer from lower accuracy and cannot be an alternative to word transcripts.We present a vocabulary independent system that can handle arbitrary queries, exploiting the information provided by having both word transcripts and phonetic transcripts. A speech recognizer generates word confusion networks and phonetic lattices. The transcripts are indexed for query processing and ranking purpose.The value of the proposed method is demonstrated by the relative high performance ofour system, which received the highest overall ranking for US English speech data in the recent NIST Spoken Term Detection evaluation.
199 citations
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23 Aug 2001
TL;DR: In this article, a method and system for obtaining consumer preferences over a communication network from consumers is presented, where the system searches the product database for products or services based on consumer's search criteria.
Abstract: A method and system for obtaining consumer preferences over a communication network from consumers. The system searches the product database for products or services based on consumer's search criteria. The system displays the products or services and/or advertisements related to the consumer's search criteria in accordance with the ranking parameter(s) specified by the user. The consumer's preferences, i.e., the search criteria and the ranking parameters(s), are stored in the database for future references, e.g., determine consumer trends, etc.
199 citations