Applying Recommendation Systems for Composing Dynamic Services for Mobile Devices

Recommendation Systems for Mobile Devices

Mobile Services Industries, Technologies, and Applications in the Global Economy - Advances in E-Business Research ◽

10.4018/978-1-4666-1981-4.ch014 ◽

2012 ◽

pp. 221-242

Author(s):

Fabrizio Caruso ◽

Giovanni Giuffrida ◽

Diego Reforgiato ◽

Calogero Zarba

Keyword(s):

Mobile Devices ◽

Online Community ◽

Collective Intelligence ◽

Recommendation Systems ◽

Similarity Criterion ◽

Limited Bandwidth ◽

Online Newspaper ◽

Geographical Position ◽

Content Similarity ◽

Average User

The authors describe three recommendation systems for online articles that are specifically tailored for mobile devices. In order to increase the number of articles read by the average user, an online newspaper could be personalized for each reader. Each user receives a personalized selection of the articles that take into account the limited bandwidth and screen, the user’s preferences, and possibly their geographical position. Two general criteria are followed: a collective intelligence criterion and a content similarity criterion. The suggested articles need to be both popular among the members of the online community, and similar to the articles already read by the user. The three systems address three similar problems. NeoPage is a tool for newspapers’ editors that suggests the position that each article should have on a web page. ARS is a tool for newspaper readers, which recommends the most similar articles to an article just read. MyNews is a tool for the readers, which produces a list of recommended articles by taking into account both the popularity of the article and the previously read articles by the user.

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Blending Augmented Reality with Real World Scenarios Using Mobile Devices

Technologies and Protocols for the Future of Internet Design ◽

10.4018/978-1-4666-0203-8.ch014 ◽

2012 ◽

pp. 258-273 ◽

Cited By ~ 1

Author(s):

Alexiei Dingli ◽

Dylan Seychell

Keyword(s):

Information Processing ◽

Augmented Reality ◽

Environmental Factors ◽

Mobile Devices ◽

Mobile Device ◽

Real World ◽

Recommendation Systems ◽

End Users ◽

The Internet ◽

The City

In this work, the authors present methods that add value to the current Web by connecting administrators of a space such as a city with its visitors. The mobile device has nowadays become an important tool in the hands of visitors of cities and the authors present it as a gateway for the administrators to their visitors. The authors present a method that processes various environmental factors during a visit and uses these factors as a context for presenting the recommendations. In this work, the authors also propose a method that can measure queues in a city, and by knowing the overall picture of the situation, it provides individual recommendations of separate mobile devices accordingly. This chapter shows, therefore, the three main steps in the process of recommendation systems: collecting information, processing the recommendations, and presenting them in an attractive way. In this case the authors focus on presenting recommendations through augmented reality in order to provide an attractive tool for end users, which would, at the end of the day, connect them further to the city over the Internet.

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Contextual and Personalized Mobile Recommendation Systems

Tools for Mobile Multimedia Programming and Development - Advances in Wireless Technologies and Telecommunication ◽

10.4018/978-1-4666-4054-2.ch005 ◽

2013 ◽

pp. 82-97

Author(s):

Jitao Sang ◽

Tao Mei ◽

Changsheng Xu ◽

Shipeng Li

Keyword(s):

Mobile Devices ◽

Large Scale ◽

Recommendation System ◽

Recommendation Systems ◽

Task Completion ◽

Mobile Data ◽

User Intent ◽

Local Business ◽

Activity Planning

Mobile devices are becoming ubiquitous. People are getting used to using their phones as a personal concierge to discover what is around and decide what to do. Mobile recommendation therefore becomes important to understand user intent and simplify task completion on the go. Since user intents essentially vary with users and sensor contexts (time and geo-location, for example), mobile recommendation needs to be both contextual and personalized. While rich user mobile data is available, such as mobile query, click-through, and check-in record, there exist two challenges in utilizing them to design a contextual and personalized mobile recommendation system: exploring characteristics from large-scale and heterogeneous mobile data and employing the uncovered characteristics for recommendation. In this chapter, the authors talk about two mobile recommendation techniques that well address the two challenges. (1) One exploits mobile query data for local business recommendation, and (2) one exploits mobile check-in record to assist activity planning.

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