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5. Dissertações de Mestrado

Type:	Dissertação
Title:	Scholar trend learner: predicting scholar popularity as early and accurate as possible
Authors:	Masoumeh Nezhadbiglari
First Advisor:	Marcos André Gonçalves
First Co-advisor:	Jussara Marques de Almeida Gonçalves
First Referee:	Alberto Henrique Frade Laender
Second Referee:	Fabrício Benevenuto de Souza
Abstract:	Prediction of scholar popularity has become an important research topic for a number of reasons. In this dissertation, we tackle the problem of predicting the popularity trend of scholars by concentrating on making predictions both as earlier and accurate as possible. In order to perform the prediction task, we first extract the popularity trends of scholars from a training set. To that end, we apply a time series clustering algorithm called K-Spectral Clustering (K-SC) to identify the popularity trends as cluster centroids. We then predict trends for scholars in a test set by solving a classification problem. Specifically, we first compute a set of measures for individual scholars based on the distance between earlier points in their particular popularity curve and the identified centroids. We then combine those distance measures with a set of academic features (e.g., number of publications, number of venues, etc) collected during the same monitoring period, and use them as input to a classification method. One aspect that distinguishes our method from other approaches is that the monitoring period, during which we gather information on each scholar popularity and academic features, is determined on a per scholar basis, as part of our approach. Using total citation count as measure of scientific popularity, we evaluate our solution on the popularity time series of more than 500,000 Computer Science scholars, gathered from Microsoft Azure Marketplace1 . The experimental results show that our prediction method outperforms other alternative prediction methods. We also show how to apply our method jointly with regression models to improve the prediction of scholar popularity values (e.g., number of citations) at a future time.
Subject:	Computação – Teses Mineração de dados Redação acadêmica – Bibliometria
language:	por
metadata.dc.publisher.country:	Brasil
Publisher:	Universidade Federal de Minas Gerais
Publisher Initials:	UFMG
metadata.dc.publisher.department:	ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
metadata.dc.publisher.program:	Programa de Pós-Graduação em Ciência da Computação
Rights:	Acesso Aberto
metadata.dc.rights.uri:	http://creativecommons.org/licenses/by-nc-nd/3.0/pt/
URI:	http://hdl.handle.net/1843/48884
Issue Date:	11-Oct-2016
Appears in Collections:	Dissertações de Mestrado

This item is licensed under a Creative Commons License