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About

I am an associate professor at the Department of Computer Science of the Faculty of Science of the University of Porto and the coordinator of LIAAD , the Artificial Intelligence and Decision Support Lab of UP. LIAAD is a unit of INESC TEC (Laboratório Associado) since 2007. I am a PhD in Computer Science by U. Porto, MSc. on Foundations of Advanced Information Technology by the Imperial Collegeand BSc. in Applied Maths and Computer Science, currently Computer Science (U. Porto). My research interests are Data Mining and Machine Learning, in particular association rules, web  and text intelligence and data mining for decision support. My past research also includes Inductive Logic Programming and Collaborative Data Mining. I lecture courses related to programming, information processing, data mining, and other areas of computing. While at the Faculty of Economics, where I stayed from 1996 to 2009, I launched, with other colleagues, the MSc. on Data Analysis and Decisison Support Systems, which I coordinated from 2000 to April 2008. I lead research projects on data mining and web intelligence. I was the director of the Masters in Computer Science at DCC-FCUP from June 2010 to August 2013. I co-chaired international conferences (ECML/PKD 2015, Discovery Science 2009, ECML/PKDD 05 and EPIA 01), workshops and seminars in data mining and artificial intelligence. I was Vice-President of APPIA the Portuguese Association for Artificial Intelligence.

Interest
Topics
Details

Details

  • Name

    Alípio Jorge
  • Cluster

    Computer Science
  • Role

    Centre Coordinator
  • Since

    01st January 2008
005
Publications

2017

Identifying top relevant dates for implicit time sensitive queries

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

Publication
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

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

Publication
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

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

Publication
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

Authors
Vinagre, J; Jorge, AM; Gama, J;

Publication
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

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

Publication
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.

Supervised
thesis

2017

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

Author
Fabiane Valéria de Oliveira Bastos Valente

Institution
UP-FCUP

2017

Workflow Recommendation for Text Classification Problems

Author
Maria João Fernandes Ferreira

Institution
UP-FCUP

2017

Identificação de termos relevantes em relatórios usando text mining

Author
Pedro da Silva Bastos

Institution
UP-FCUP

2017

Automatic Coherence Evaluation Applied to Topic Models

Author
Arian Rodrigo Pasquali

Institution
UP-FCUP

2016

Recommender System for an e-learning platform

Author
Ricardo Jorge Mendes Oliveira

Institution
UP-FCUP