Matches in ScholarlyData for { ?s ?p ?o. }
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- item-3 hasContent victor-carneiro.
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- item-6 hasContent fabrizio-silvestri.
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- 118 type InProceedings.
- 118 label "A Probabilistic Model Based Approach for Blended Search".
- 118 sameAs 118.
- 118 abstract "In this paper, we propose to model the blended search problem by assuming conditional dependencies among queries, VSEs and search results. The probability distributions of this model are learned from search engine query log through unigram language model. Our experimental exploration shows that, (1) a large number of queries in generic Web search have vertical search intentions; and (2) our proposed algorithm can effectively blend vertical search results into generic Web search, which can improve the Mean Average Precision (MAP) by as much as 16% compared to traditional Web search without blending. these components into a single list. However, from the classical meta-search problem's configuration, the query log of component search engines is not available for study. In this extended abstract, we model the blended search problem based on the conditional dependencies among queries, VSEs and all the search results. We utilize the usage information, i.e. query log, of all the VSEs, which are not available for traditional metasearch engines, to learn the model parameters by the smoothed unigram language model. Finally, given a user query, the search results from both generic Web search and different VSEs are ranked together by inferring their probabilities of relevance to the given query. The main contributions of this work are, (1) through studying the belonging vertical search engines' query log of a commercial search engine, we show the importance of blended search problem; (2) we propose a novel probabilistic model based approach to explore the blended search problem; and (3) we experimentally verify that our proposed algorithm can effectively blend vertical search results into generic Web search, which can improve the MAP as much as 16% in contrast to traditional Web search without vertical search blending and 10% to some other some ranking baseline.".
- 118 hasAuthorList authorList.
- 118 isPartOf proceedings.
- 118 keyword "Poster Session".
- 118 title "A Probabilistic Model Based Approach for Blended Search".
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- item-1 hasContent ning-liu.
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- item-2 hasContent jun-yan.
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- item-3 hasContent zheng-chen.
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- 119 type InProceedings.
- 119 label "Bucefalo: A Tool for Intelligent Search and Filtering for Web-based Personal Health Records".
- 119 sameAs 119.
- 119 abstract "In this poster, a tool named BUCEFALO is presented. This tool is specially designed to improve the information retrieval tasks in web-based Personal Health Records (PHR). This tool implements semantic and multilingual query expansion techniques and information filtering algorithms in order to help users find the most valuable information about a specific clinical case. The filtering model is based on fuzzy prototypes based filtering, data quality measures, user profiles and healthcare ontologies. The first experimental results illustrate the feasibility of this tool. standards the relevant health information is reliably and unambiguously tagged using XML within a single file. The use of XML allows that this information can be read, understood and processed for any application which uses the standard. Google Health and Microsoft Health use a subset of the CCR (Continuity of Care Record) standard. The CCR standard is the most used patient health summary. A document in CCR format is a XML document that consists of a header, a footer, and a body of health data organized into as many as 17 sections, e.g. problems and conditions, medications list, allergies list, family history, procedures, encounters, etc.. These web-based PHRs are examples of multi-user document repositories. The clinical reports can be read for different users (nurses, physicians, students) and for different purposes (diagnosis, learning, research). When a document repository has many users and many purposes, there are different points of view of the same repository structure. Therefore, it is necessary a technique able to manage these different points of view in knowledge retrieval tasks. In this case, fuzzy logic is especially recommendable due to its special features to model information retrieval applications.".
- 119 hasAuthorList authorList.
- 119 isPartOf proceedings.
- 119 keyword "Poster Session".
- 119 title "Bucefalo: A Tool for Intelligent Search and Filtering for Web-based Personal Health Records".
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- item-1 hasContent francisco-p-romero.
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- item-2 hasContent jesus-serrano-guerrero.
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- item-3 hasContent jose-a-olivas.
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- 12 type InProceedings.
- 12 label "Matchbox: Large Scale Online Bayesian Recommendations".
- 12 sameAs 12.
- 12 abstract "We present a probabilistic model for generating personalised recommendations of items to users of a web service. The Matchbox system makes use of content information in the form of user and item meta data in combination with col- laborative filtering information from previous user behavior in order to predict the value of an item for a user. Users and items are represented by feature vectors which are mapped into a low-dimensional ‘trait space' in which similarity is measured in terms of inner products. The model can be trained from different types of feedback in order to learn user-item preferences. Here we present three alternatives: direct observation of an absolute rating each user gives to some items, observation of a binary preference (like/ don't like) and observation of a set of ordinal ratings on a user- specific scale. Efficient inference is achieved by approxi- mate message passing involving a combination of Expecta- tion Propagation (EP) and Variational Message Passing. We also include a dynamics model which allows an item's popu- larity, a user's taste or a user's personal rating scale to drift over time. By using Assumed-Density Filtering (ADF) for training, the model requires only a single pass through the training data. This is an on-line learning algorithm capable of incrementally taking account of new data so the system can immediately reflect the latest user preferences. We eval- uate the performance of the algorithm on the MovieLens and Netflix data sets consisting of approximately 1,000,000 and 100,000,000 ratings respectively. This demonstrates that training the model using the on-line ADF approach yields state-of-the-art performance with the option of improving performance further if computational resources are available by performing multiple EP passes over the training data.".
- 12 hasAuthorList authorList.
- 12 isPartOf proceedings.
- 12 keyword "Data Mining".
- 12 title "Matchbox: Large Scale Online Bayesian Recommendations".
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- item-1 hasContent david-stern.
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- item-2 hasContent ralf-herbrich.
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- item-3 type ListItem.
- item-3 hasContent thore-graepel.
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- 120 type InProceedings.
- 120 label "Dataplorer: A Scalable Search Engine for the Data Web".
- 120 sameAs 120.
- 120 abstract "More and more structured information in the form of semantic data is nowadays available. It offers a wide range of new possibilities especially for semantic search and Web data integration. However, their effective exploitation still brings about a number of challenges, e.g. usability, scalability and uncertainty. In this paper, we present Dataplorer, a solution designed to address these challenges. We consider the usability through the use of hybrid queries and faceted search, while still preserving the scalability thanks to an extension of inverted index to support this type of query. Moreover, Dataplorer deals with uncertainty by means of a powerful ranking scheme to find relevant results. Our experimental results show that our proposed approach is promising and it makes us believe that it is possible to extend the current IR infrastructure to query and search the Web of data. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms: Algorithms, Performance, Experimentation Keywords: hybrid query, inverted index, ranking, faceted search sake of the others. The usability challenge is addressed by providing the user with hybrid query capabilities, leveraging the power of structured queries and the ease of use of keyword search. We also propose a faceted search functionality that allows users to progressively compose the structured part of their information need after having started with imprecise keywords. Scalability is one of the main challenges that hybrid queries are facing, due to the large amount of data. Inspired from the cross field of DB and IR integration, we make IR compatible with hybrid search through an extension of the inverted index, and thus able to scale as well as to handle structured information. To ensure that uncertainty does not remain as a problem to return relevant results, we provide a powerful ranking scheme that considers structures of both data and hybrid queries for score propagation and aggregation during results ranking. As an improvement of our previous work [3], we support faceted search with integrated ranking to tackle both usability and uncertainty issues while preserving efficiency.".
- 120 hasAuthorList authorList.
- 120 isPartOf proceedings.
- 120 keyword "Poster Session".
- 120 title "Dataplorer: A Scalable Search Engine for the Data Web".
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