Barreiro Garrido, Álvaro - Portal de Investigación del Consorcio Madroño

SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL (p. 735-736) - 1/1/2010

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Blog Snippets: A Comments-Biased Approach

Parapar, Javier
Lopez-Castro, Jorge
Barreiro, Alvaro;

SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL (p. 711-712) - 1/1/2010

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Using a Rank Fusion Technique to Improve Shot Boundary Detection Effectiveness

Eduardo Ares, M.
Barreiro, Alvaro;

Lecture Notes In Computer Science (p. 247-254) - 1/1/2009

ISSN 03029743

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Avoiding Bias in Text Clustering Using Constrained K-means and May-Not-Links

Eduardo Ares, M.
Parapar, Javier
Barreiro, Alvaro;

Lecture Notes In Computer Science (p. 322-329) - 1/1/2009

ISSN 03029743

iMarina

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Última actualización de los datos: 12/08/23 3:08

Barreiro Garrido, Álvaro alvaro.barreiro@urjc.es

Relevance feedback for building pooled test collections

Automatic depression score estimation with word embedding models

Statistical language models for query-by-example spoken document retrieval

Using Score Distributions to Compare Statistical Significance Tests for Information Retrieval Evaluation

Collaborative filtering embeddings for memory-based recommender systems

Efficient query-by-example spoken document retrieval combining phone multigram representation and dynamic time warping

When to stop making relevance judgments? A study of stopping methods for building information retrieval test collections

Document-based and Term-based Linear Methods for Pseudo-Relevance Feedback

Finding and analysing good neighbourhoods to improve collaborative filtering

A MapReduce implementation of posterior probability clustering and relevance models for recommendation

PsyProf: A Platform for Assisted Screening of Depression in Social Media

LiMe: Linear Methods for Pseudo-Relevance Feedback

Combining Top-N Recommenders with Metasearch Algorithms

Promoting Divergent Terms in the Estimation of Relevance Models

A Cluster Based Pseudo Feedback Technique Which Exploits Good and Bad Clusters

Improving Alternative Text Clustering Quality in the Avoiding Bias Task with Spectral and Flat Partition Algorithms

Where to Start Filtering Redundancy? A Cluster-Based Approach

Blog Snippets: A Comments-Biased Approach

Using a Rank Fusion Technique to Improve Shot Boundary Detection Effectiveness

Avoiding Bias in Text Clustering Using Constrained K-means and May-Not-Links

Grupos de investigación

Perfiles de investigador/a

Barreiro Garrido, Álvaro alvaro.barreiro@urjc.es

Actividades

Relevance feedback for building pooled test collections

Automatic depression score estimation with word embedding models

Statistical language models for query-by-example spoken document retrieval

Using Score Distributions to Compare Statistical Significance Tests for Information Retrieval Evaluation

Collaborative filtering embeddings for memory-based recommender systems

Efficient query-by-example spoken document retrieval combining phone multigram representation and dynamic time warping

When to stop making relevance judgments? A study of stopping methods for building information retrieval test collections

Document-based and Term-based Linear Methods for Pseudo-Relevance Feedback

Finding and analysing good neighbourhoods to improve collaborative filtering

A MapReduce implementation of posterior probability clustering and relevance models for recommendation

PsyProf: A Platform for Assisted Screening of Depression in Social Media

LiMe: Linear Methods for Pseudo-Relevance Feedback

Combining Top-N Recommenders with Metasearch Algorithms

Promoting Divergent Terms in the Estimation of Relevance Models

A Cluster Based Pseudo Feedback Technique Which Exploits Good and Bad Clusters

Improving Alternative Text Clustering Quality in the Avoiding Bias Task with Spectral and Flat Partition Algorithms

Where to Start Filtering Redundancy? A Cluster-Based Approach

Blog Snippets: A Comments-Biased Approach

Using a Rank Fusion Technique to Improve Shot Boundary Detection Effectiveness

Avoiding Bias in Text Clustering Using Constrained K-means and May-Not-Links

Grupos de investigación

Perfiles de investigador/a