PMC - CaMRS SI 2016 : Elsevier Pervasive and Mobile Computing [IF=1.667] - Special Issue on Context-aware Mobile Recommender Systems
Call For Papers
Special Issue on Context-aware Mobile Recommender Systems in Pervasive and Mobile Computing, Elsevier (Impact factor 1.667 according to Thomson Scientific 2013 Journal Citations Report)
Special issue publication date: 2016
Recommender systems are software tools and techniques providing suggestions for items to be of use to a user. They are considered to be a kind of Information Retrieval (IR) system providing personalized information recommendations. In fact, since the appearance of the first commercial Recommender System called “Tapestry” in 1992, Recommender Systems have proven effective in overcoming the challenges related to the incredible growth of the Web, namely information overload. In such a context, Recommender Systems are especially valuable tools for nonexperienced users facing decision-making processes as it is well-demonstrated by the increasingly common appearance of e-commerce Websites taking advantage of recommendation techniques.
Current advances in Mobile Computing research and wireless network technologies along with the proliferation of evermore powerful mobile devices such as smartphones and tablet computers has brought recommender systems to migrate to mobile platforms, resulting in the Mobile Recommender Systems research field. In detail, beyond the possibility of access recommender systems at anytime and anywhere (ubiquity) as mobile applications, the diversification of the capabilities of mobile devices towards the concept of self-awareness has led built-in sensors such as geolocation, motion and environmental sensors to play a role in the development of mobile recommender systems.
The availability of location data from positioning systems such as Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) has become more and more common among mobile devices. Nowadays, location information could be the most exploited kind of contextual information among mobile applications. In the field of mobile recommender systems, it is giving rise to the coining of the term geo-recommendation to refer to the ability to recommend places of possible interest to a user taking into account both the current geographical location of the user and the geographical location of the places. Geo-recommendation is redefining the way in-door shopping is performed bringing notable opportunities to the leisure domain. These new opportunities are been leveraged by social networking services such as Foursquare, Swarm and Yelp to offer personalized recommendation services that help customers to make more informed decisions about, for example, where to eat, shop and relax.
The aim of this special issue is to explore the recent advances in the application of location-based recommendation solutions in different domains soliciting original scientific contributions in the form of theoretical foundations, comparative analysis, surveys, case studies, techniques and tools of context-aware mobile recommender systems.
The topics of this special issue include but are not limited to:
* Ontology-driven context-aware mobile recommender systems
* Case-based reasoning context-aware mobile recommender systems
* Constraint-based context-aware mobile recommender systems
* Knowledge-based context-aware mobile recommender systems
* Agent-based context-aware mobile recommender system architectures
* Peer-to-peer context-aware mobile recommender systems
* Ad-hoc network-based approaches for context aware mobile recommender systems
* Decentralized collaboration approaches for context-aware mobile recommender systems
* Cloud-based context-aware mobile recommender systems
* Social networking-based context-aware mobile recommender systems
* Service-oriented context-aware mobile recommender system architectures
* Event-driven context-aware mobile recommender system architectures
* Geographic Information System (GIS)-based techniques for context-aware mobile recommendations
* Multi-modal context-aware mobile recommender systems
* In-door and out-door positioning techniques for mobile recommendations
* Mobile Route recommendation systems
* Graph optimization algorithms for route recommendations in mobile environments
* Clustering algorithms for context-aware mobile recommendations
* Performance metrics for context-aware mobile recommender systems
* Qualitative evaluation techniques for context-aware mobile recommender systems
* Context modeling approaches for context-aware mobile recommendations
All submissions have to be prepared according to the Guide for Authors as published in the Journal website at http://www.ees.elsevier.com/pmc/. Authors should select “SI: Recom. Sys.”, from the “Choose Article Type” pull-down menu during the submission process. All contributions must not have been previously published or be under consideration for publication elsewhere. A submission based on one or more papers that appeared elsewhere has to comprise major value-added extensions over what appeared previously (at least 30% new material). Authors are requested to attach to the submitted paper their relevant, previously published articles and a summary document explaining the enhancements made in the journal version.
Luis Omar Colombo-Mendoza, Universidad de Murcia, Spain (firstname.lastname@example.org)
Rafael Valencia-García, Universidad de Murcia, Spain (email@example.com)
Giner Alor-Hernández, Instituto Tecnológico de Orizaba, México (firstname.lastname@example.org)
Paolo Bellavista, University of Bologna, Italy (email@example.com)