Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Effective feature reduction for link prediction in location-based social networks
Date
2019-10-01
Author
Bayrak, Ahmet Engin
Polat, Faruk
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
283
views
0
downloads
Cite This
In this study, we investigated feature-based approaches for improving the link prediction performance for location-based social networks (LBSNs) and analysed their performances. We developed new features based on time, common friend detail and place category information of check-in data in order to make use of information in the data which cannot be utilised by the existing features from the literature. We proposed a feature selection method to determine a feature subset that enhances the prediction performance with the removal of redundant features by clustering them. After clustering features, a genetic algorithm is used to determine the ones to select from each cluster. A non-monotonic and feasible feature selection is ensured by the proposed genetic algorithm. Results depict that both new features and the proposed feature selection method improved link prediction performance for LBSNs.
Subject Keywords
Library and Information Sciences
,
Information Systems
URI
https://hdl.handle.net/11511/41892
Journal
JOURNAL OF INFORMATION SCIENCE
DOI
https://doi.org/10.1177/0165551518808200
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Success factors of software development in a distributed setting : a collective case study
Bulğurcu, Burcu; Bilgen, Semih; Department of Information Systems (2006)
This study represents an attempt to address and discuss some of the most significant questions in the research on virtual software development work today. The research is held as a collective case study, including three cases with distinctive characteristics in both the software projects investigated and the types of collaborations. The study aims to reveal the success factors in virtual work, especially on the issues of communication, coordination and collaboration, by presenting the unfavorable experience...
Exploring the relationship between web presence and web usability for universities A case study from Turkey
Peker, Serhat; Kucukozer-Cavdar, Seyma; Çağıltay, Kürşat (Emerald, 2016-01-01)
Purpose - The purpose of this paper is to statistically explore the relationship between web usability and web presence of the universities. As a case study, five Turkish universities in different rankings which were selected from Webometrics rankings were evaluated and compared.
A quantitative investigation of students' attitudes towards electronic book technology
Bulur, Hatice Gonca (SAGE Publications, 2020-09-01)
The purpose of this study is to analyse the factors that have an impact on technology adoption for e-books utilizing the Analytic Hierarchy Process and Multiple Regression Analysis methods. Findings indicate that perceived usefulness and ease of use are the most significant determinants in using e-books. Of key significance is that Analytic Hierarchy Process results show that consumers make pairwise comparisons, adding environmental concerns to the selection process. Recognizing the importance of all these ...
Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data
Erdogdu, Utku; TAN, MEHMET; Alhajj, Reda; Polat, Faruk; Rokne, Jon; Demetrick, Douglas (Inderscience Publishers, 2013-01-01)
The availability of enough samples for effective analysis and knowledge discovery has been a challenge in the research community, especially in the area of gene expression data analysis. Thus, the approaches being developed for data analysis have mostly suffered from the lack of enough data to train and test the constructed models. We argue that the process of sample generation could be successfully automated by employing some sophisticated machine learning techniques. An automated sample generation framewo...
Explicit diversification of search results across multiple dimensions for educational search
Yigit-Sert, Sevgi; Altıngövde, İsmail Sengör; Macdonald, Craig; Ounis, Iadh; ULUSOY, ÖZGÜR (Wiley, 2020-09-01)
Making use of search systems to foster learning is an emerging research trend known assearch as learning. Earlier works identified result diversification as a useful technique to support learning-oriented search, since diversification ensures a comprehensive coverage of various aspects of the queried topic in the result list. Inspired by this finding, first we define a new research problem, multidimensional result diversification, in the context of educational search. We argue that in a search engine for th...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
A. E. Bayrak and F. Polat, “Effective feature reduction for link prediction in location-based social networks,”
JOURNAL OF INFORMATION SCIENCE
, pp. 676–690, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41892.