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Sobre

Sobre

Sou professor associado do Departamento de Ciência de Computadores da Faculdade de Ciências da Universidade do Porto e coordenador do LIAAD, Laboratório de Inteligência Artificial e de Apoio à Decisão da UP. O LIAAD é um cenrto do INESC TEC desde 2007. Sou doutor em Ciência da Computação pela U. Porto, MSc. em Fundamentos de Tecnologia de Informação Avançada pelo Imperial College e Lic. Em Matemática Aplicada ramo Ciência de Computadores (U. Porto). Os meus interesses de investigação são Extração de Conhecimento (Data Mining) e Aprendizagem Automática (Machine Learning), em particular regras de associação, text mining e sistemas de recomendação. A minha investigação anterior inclui programação em lógica indutiva e data miing colaborativo. Eu leciono cursos relacionados com programação, processamento de informação, data mining e outras áreas da computação. Enquanto na Faculdade de Economia, onde permaneci de 1996 a 2009, lancei, com outros colegas, o mestrado em Análise de Dados e Sistemas de Apoio à Decisão (MADSAD), que coordenei de 2000 a Abril de 2008. Dirijo projetos em data mining e inteligência na web. Fui diretor do Mestrado em Ciência dos Computadores no DCC-FCUP de junho de 2010 a agosto de 2013. Co-organizei conferências internacionais (ECML / PKD 2015, Discovery Science 2009, ECML / PKDD 05 e EPIA 01), workshops e seminários em data mining e inteligência artificial. Fui Vice-Presidente da APPIA Associação Portuguesa para a Inteligência Artificial.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Alípio Jorge
  • Cluster

    Informática
  • Cargo

    Coordenador de Centro
  • Desde

    01 janeiro 2008
017
Publicações

2017

Identifying top relevant dates for implicit time sensitive queries

Autores
Campos, R; Dias, G; Jorge, AM; Nunes, C;

Publicação
Inf. Retr. Journal

Abstract
Despite a clear improvement of search and retrieval temporal applications, current search engines are still mostly unaware of the temporal dimension. Indeed, in most cases, systems are limited to offering the user the chance to restrict the search to a particular time period or to simply rely on an explicitly specified time span. If the user is not explicit in his/her search intents (e.g., “philip seymour hoffman”) search engines may likely fail to present an overall historic perspective of the topic. In most such cases, they are limited to retrieving the most recent results. One possible solution to this shortcoming is to understand the different time periods of the query. In this context, most state-of-the-art methodologies consider any occurrence of temporal expressions in web documents and other web data as equally relevant to an implicit time sensitive query. To approach this problem in a more adequate manner, we propose in this paper the detection of relevant temporal expressions to the query. Unlike previous metadata and query log-based approaches, we show how to achieve this goal based on information extracted from document content. However, instead of simply focusing on the detection of the most obvious date we are also interested in retrieving the set of dates that are relevant to the query. Towards this goal, we define a general similarity measure that makes use of co-occurrences of words and years based on corpus statistics and a classification methodology that is able to identify the set of top relevant dates for a given implicit time sensitive query, while filtering out the non-relevant ones. Through extensive experimental evaluation, we mean to demonstrate that our approach offers promising results in the field of temporal information retrieval (T-IR), as demonstrated by the experiments conducted over several baselines on web corpora collections. © 2017 Springer Science+Business Media New York

2016

GTE-Rank: A time-aware search engine to answer time-sensitive queries

Autores
Campos, R; Dias, G; Jorge, A; Nunes, C;

Publicação
INFORMATION PROCESSING & MANAGEMENT

Abstract
In the web environment, most of the queries issued by users are implicit by nature. Inferring the different temporal intents of this type of query enhances the overall temporal part of the web search results. Previous works tackling this problem usually focused on news queries, where the retrieval of the most recent results related to the query are usually sufficient to meet the user's information needs. However, few works have studied the importance of time in queries such as "Philip Seymour Hoffman" where the results may require no recency at all. In this work, we focus on this type of queries named "time-sensitive queries" where the results are preferably from a diversified time span, not necessarily the most recent one. Unlike related work, we follow a content-based approach to identify the most important time periods of the query and integrate time into a re-ranking model to boost the retrieval of documents whose contents match the query time period. For that purpose, we define a linear combination of topical and temporal scores, which reflects the relevance of any web document both in the topical and temporal dimensions, thus contributing to improve the effectiveness of the ranked results across different types of queries. Our approach relies on a novel temporal similarity measure that is capable of determining the most important dates for a query, while filtering out the non-relevant ones. Through extensive experimental evaluation over web corpora, we show that our model offers promising results compared to baseline approaches. As a result of our investigation, we publicly provide a set of web services and a web search interface so that the system can be graphically explored by the research community.

2015

Improving the accuracy of long-term travel time prediction using heterogeneous ensembles

Autores
Mendes Moreira, J; Jorge, AM; de Sousa, JF; Soares, C;

Publicação
NEUROCOMPUTING

Abstract
This paper is about long-term travel time prediction in public transportation. However, it can be useful for a wider area of applications. It follows a heterogeneous ensemble approach with dynamic selection. A vast set of experiments with a pool of 128 tuples of algorithms and parameter sets (a&ps) has been conducted for each of the six studied routes. Three different algorithms, namely, random forest, projection pursuit regression and support vector machines, were used. Then, ensembles of different sizes were obtained after a pruning step. The best approach to combine the outputs is also addressed. Finally, the best ensemble approach for each of the six routes is compared with the best individual a&ps. The results confirm that heterogeneous ensembles are adequate for long-term travel time prediction. Namely, they achieve both higher accuracy and robustness along time than state-of-the-art learners.

2015

An overview on the exploitation of time in collaborative filtering

Autores
Vinagre, J; Jorge, AM; Gama, J;

Publicação
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
Classic Collaborative Filtering (CF) algorithms rely on the assumption that data are static and we usually disregard the temporal effects in natural user-generated data. These temporal effects include user preference drifts and shifts, seasonal effects, inclusion of new users, and items entering the systemand old ones leavinguser and item activity rate fluctuations and other similar time-related phenomena. These phenomena continuously change the underlying relations between users and items that recommendation algorithms essentially try to capture. In the past few years, a new generation of CF algorithms has emerged, using the time dimension as a key factor to improve recommendation models. In this overview, we present a comprehensive analysis of these algorithms and identify important challenges to be faced in the near future.(C) 2015 John Wiley & Sons, Ltd.

2015

Survey of Temporal Information Retrieval and Related Applications

Autores
Campos, R; Dias, G; Jorge, AM; Jatowt, A;

Publicação
ACM COMPUTING SURVEYS

Abstract
Temporal information retrieval has been a topic of great interest in recent years. Its purpose is to improve the effectiveness of information retrieval methods by exploiting temporal information in documents and queries. In this article, we present a survey of the existing literature on temporal information retrieval. In addition to giving an overview of the field, we categorize the relevant research, describe the main contributions, and compare different approaches. We organize existing research to provide a coherent view, discuss several open issues, and point out some possible future research directions in this area. Despite significant advances, the area lacks a systematic arrangement of prior efforts and an overview of state-of-the-art approaches. Moreover, an effective end-to-end temporal retrieval system that exploits temporal information to improve the quality of the presented results remains undeveloped.

Teses
supervisionadas

2017

APP Consumer Response: A.I. Modelling Towards Optimal Managerial Decisions in Mobile Marketing.

Autor
Fabiane Valéria de Oliveira Bastos Valente

Instituição
UP-FCUP

2017

Workflow Recommendation for Text Classification Problems

Autor
Maria João Fernandes Ferreira

Instituição
UP-FCUP

2017

Identificação de termos relevantes em relatórios usando text mining

Autor
Pedro da Silva Bastos

Instituição
UP-FCUP

2017

Automatic Coherence Evaluation Applied to Topic Models

Autor
Arian Rodrigo Pasquali

Instituição
UP-FCUP

2016

Clustering de relacionamentos entre entidades nomeadas em textos com base no contexto

Autor
Nelson Alves Morais

Instituição
UP-FCUP