The role of location and social strength for friendship prediction in location-based social networks. Issue 4 (July 2018)
- Record Type:
- Journal Article
- Title:
- The role of location and social strength for friendship prediction in location-based social networks. Issue 4 (July 2018)
- Main Title:
- The role of location and social strength for friendship prediction in location-based social networks
- Authors:
- Valverde-Rebaza, Jorge C.
Roche, Mathieu
Poncelet, Pascal
Lopes, Alneu de Andrade - Abstract:
- Highlights: A taxonomy for friendship prediction methods for location-based social networks is proposed. Five new methods for friendship prediction in location-based social networks are proposed. A comprehensive analysis of selected current methods on link prediction for LBSNs is carried out. From a set of fifteen friendship prediction methods, we identified the top-5 methods with feasibility for real-world applications. Two of our proposals are in this top-5. Abstract: Recent advances in data mining and machine learning techniques are focused on exploiting location data. These advances, combined with the increased availability of location-acquisition technology, have encouraged social networking services to offer to their users different ways to share their location information. These social networks, called location-based social networks (LBSNs), have attracted millions of users and the attention of the research community. One fundamental task in the LBSN context is the friendship prediction due to its role in different applications such as recommendation systems. In the literature exists a variety of friendship prediction methods for LBSNs, but most of them give more importance to the location information of users and disregard the strength of relationships existing between these users. The contributions of this article are threefold, we: 1) carried out a comprehensive survey of methods for friendship prediction in LBSNs and proposed a taxonomy to organize the existingHighlights: A taxonomy for friendship prediction methods for location-based social networks is proposed. Five new methods for friendship prediction in location-based social networks are proposed. A comprehensive analysis of selected current methods on link prediction for LBSNs is carried out. From a set of fifteen friendship prediction methods, we identified the top-5 methods with feasibility for real-world applications. Two of our proposals are in this top-5. Abstract: Recent advances in data mining and machine learning techniques are focused on exploiting location data. These advances, combined with the increased availability of location-acquisition technology, have encouraged social networking services to offer to their users different ways to share their location information. These social networks, called location-based social networks (LBSNs), have attracted millions of users and the attention of the research community. One fundamental task in the LBSN context is the friendship prediction due to its role in different applications such as recommendation systems. In the literature exists a variety of friendship prediction methods for LBSNs, but most of them give more importance to the location information of users and disregard the strength of relationships existing between these users. The contributions of this article are threefold, we: 1) carried out a comprehensive survey of methods for friendship prediction in LBSNs and proposed a taxonomy to organize the existing methods; 2) put forward a proposal of five new methods addressing gaps identified in our survey while striving to find a balance between optimizing computational resources and improving the predictive power; and 3) used a comprehensive evaluation to quantify the prediction abilities of ten current methods and our five proposals and selected the top-5 friendship prediction methods for LBSNs. We thus present a general panorama of friendship prediction task in the LBSN domain with balanced depth so as to facilitate research and real-world application design regarding this important issue. … (more)
- Is Part Of:
- Information processing & management. Volume 54:Issue 4(2018:Jul.)
- Journal:
- Information processing & management
- Issue:
- Volume 54:Issue 4(2018:Jul.)
- Issue Display:
- Volume 54, Issue 4 (2018)
- Year:
- 2018
- Volume:
- 54
- Issue:
- 4
- Issue Sort Value:
- 2018-0054-0004-0000
- Page Start:
- 475
- Page End:
- 489
- Publication Date:
- 2018-07
- Subjects:
- Location-based social networks -- Link prediction -- Friendship recommendation -- Human mobility -- User behavior
Information storage and retrieval systems -- Periodicals
Information science -- Periodicals
Systèmes d'information -- Périodiques
Sciences de l'information -- Périodiques
Information science
Information storage and retrieval systems
Periodicals
658.4038 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03064573 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.ipm.2018.02.004 ↗
- Languages:
- English
- ISSNs:
- 0306-4573
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4493.893000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 6485.xml