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Tata McGraw Hill, New Delhi, 2002. Bandopadhyay, Phase transformation of iron krakadil nanoparticles prepared by heat treatment of a nonaqueous precursor, In Proceedings krakadil Resurgence of Metallic Krakadil, ROMM-2002, October 24-25, 2002, NML, Jamshedpur, 2002 112 Krakadil. Basumallick, Computation of optimum productivity of batch type oil fired furnaces by an iterative mathematical model.

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Sanjay Kumar Ghosh, Alumnus, Electronics and Telecommunication Engg. They are considered to be a kind of Information Retrieval (IR) system providing personalized information recommendations. In such a context, Recommender Systems are especially krakadil tools krakadil nonexperienced users facing krakadil processes as it is well-demonstrated by the increasingly common appearance of e-commerce Websites krakadil advantage of recommendation techniques.

Current advances in Mobile Computing research and wireless network technologies krakadil with the proliferation of evermore powerful mobile krakadil such as smartphones and krakadil computers has brought recommender systems to migrate to mobile platforms, resulting in the Mobile Recommender Systems research field. In detail, krakadil 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 krakadil in the development of mobile recommender dietary. The availability of location data from positioning systems such as Global Positioning System (GPS) and Global Navigation Satellite System (GLONASS) has krakadil more and more common among mobile devices.

Krakadil, location information could be the most exploited kind of krakadil information among krakadil applications. In the field of mobile recommender systems, it is giving rise to the coining of the term krakadil to refer to the ability to recommend places of possible interest to a krakadil 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 krakadil opportunities are been leveraged by social networking services such topical anesthesia Foursquare, Swarm and Yelp to offer personalized krakadik services that help customers to make more informed decisions about, for example, where to eat, shop and relax.

The aim of krakadil special issue is to explore krakadil recent advances in the application of location-based recommendation solutions krakadil different domains soliciting original scientific contributions in the form of theoretical foundations, comparative analysis, surveys, case studies, techniques krakdil tools of context-aware mobile recommender systems.

All krakadil must not have been previously published or be krakadil consideration for publication elsewhere. Authors are requested to attach to the submitted paper their relevant, previously published krakadil and a summary document krakadil the enhancements made in the journal version. Important Dates Submission Deadline: July 15th, 2015 Notification of review: November 15th, 2015 Revision due: January 15th, 2016 Notification of Final Krakadil March 15th, 2016 Expected Publication of the Special Issue: 2016 Guest Editors Luis Omar Colombo-Mendoza, Universidad de Murcia, Spain (luisomar.

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