Mobile recommender systems: An overview of technologies and challenges <s> III. MOBILE ENVIRONMENTS <s> In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems. <s> BIB001 </s> Mobile recommender systems: An overview of technologies and challenges <s> III. MOBILE ENVIRONMENTS <s> Examining several sources of data on smartphone use, this paper presents evidence for the popular conjecture that mobile devices are "habit-forming." The form of habits we identified is called a checking habit: brief, repetitive inspection of dynamic content quickly accessible on the device. We describe findings on kinds and frequencies of checking behaviors in three studies. We found that checking habits occasionally spur users to do other things with the device and may increase usage overall. Data from a controlled field experiment show that checking behaviors emerge and are reinforced by informational "rewards" that are very quickly accessible. Qualitative data suggest that although repetitive habitual use is frequent, it is experienced more as an annoyance than an addiction. We conclude that supporting habit-formation is an opportunity for making smartphones more "personal" and "pervasive." <s> BIB002 </s> Mobile recommender systems: An overview of technologies and challenges <s> III. MOBILE ENVIRONMENTS <s> Privacy and personalization of mobile experiences are inherently in conflict: better personalization demands knowing more about the user, potentially violating user privacy. A promising approach to mitigate this tension is to migrate personalization to the client, an approach dubbed client-side personalization. This paper advocates for operating system support for client-side personalization and describes MoRePriv, an operating system service implemented in the Windows Phone OS. We argue that personalization support should be as ubiquitous as location support, and should be provided by a unified system within the OS, instead of by individual apps. We aim to provide a solution that will stoke innovation around mobile personalization. To enable easy application personalization, MoRePriv approximates users' interests using personae such as technophile or business executive. Using a number of case studies and crowd-sourced user studies, we illustrate how more complex personalization tasks can be achieved using MoRePriv. For privacy protection, MoRePriv distills sensitive user information to a coarse-grained profile, which limits the potential damage from information leaks. We see MoRePriv as a way to increase end-user privacy by enabling client-side computing, thus minimizing the need to share user data with the server. As such, MoRePriv shepherds the ecosystem towards a better privacy stance by nudging developers away from today's privacy-violating practices. Furthermore, MoRePriv can be combined with privacy-enhancing technologies and is complimentary to recent advances in data leak detection. <s> BIB003
In the internet era and with personal computers being everywhere the need for e-commerce and recommender technologies is essential. Likewise, smartphones and tablets use the same technologies in their respective environments with extra parameters taken into consideration. Such parameters include location, time and screen limitations. The information overload is so high nowadays, that recommender systems are necessary in almost every aspect of mobile devices. Furthermore, examples of recommendations include mobile commerce, news reading and finding related services (such as hotels or other tourism related information). Additionally, the personalization of the operating system itself has been taken into consideration BIB002  . Moreover mobile devices have progressed so fast that tend to become the primary source of access to social networks BIB002 . Users who want instant access, from everywhere and not use a computer to do that, tend to use a mobile device such as a smartphone or tablet BIB002 . The network communication facilities such as the cellular, Wi-Fi and GPS have aided towards this direction. The progress both in hardware and software have enabled mobile devices to offer extraordinary services and help people to achieve experiences that otherwise would be unknown to them. Context-awareness is the most important aspect that drives mobile devices  BIB002 . Mobile devices are based to services in order to support their users. These services are location-based, user-based (such as the use of personal profiles), device-based (such as the memory, processor speed and screen size), spatio-based attributes (such as day, night, mood and/or weather conditions) and social-based attributes based on communicating with other users BIB001 . In mobile devices there are other factors than the traditional, which should be taken seriously in order to improve recommendations. Such elements include access to the e-mail agent of the OS, to the call logs, SMS excavating, internet and online chat services history and the use of a user well-defined profile  BIB003 . Additionally, a very serious aspect is the use of data from social networks, which nowadays its use is put forward mobile operating systems that have integrated features of networks such as Facebook and twitter  [1, 17, 20, 23] . Also, the use purchase history data from large auction sites such as EBay must be taken into consideration. A very important aspect in respect to the usage of mobile devices and recommendation system to be more specific is privacy. It is the main cause that mobile users avoid to use such technologies. It is a challenge to the use of recommenders in mobile environments and it is discussed further in the challenges section below.