Mobile recommender systems: An overview of technologies and challenges <s> INTRODUCTION <s> Recommender systems are widely used to help deal with the problem of information overload. However, recommenders raise serious privacy and security issues. The personal information collected by recommenders raises the risk of unwanted exposure of that information. Also, malicious users can bias or sabotage the recommendations that are provided to other users. This paper raises important research questions in three topics relating to exposure and bias in recommender systems: the value and risks of the preference information shared with a recommender, the effectiveness of shilling attacks designed to bias a recommender, and the issues involved in distributed or peer-to-peer recommenders. The goal of the paper is to bring these questions to the attention of the information and communication security community, to invite their expertise in addressing them. <s> BIB001 </s> Mobile recommender systems: An overview of technologies and challenges <s> INTRODUCTION <s> The idea that context is important when predicting customer behavior has been maintained by scholars in marketing and data mining. However, no systematic study measuring how much the contextual information really matters in building customer models in personalization applications has been done before. In this paper, we study how important the contextual information is when predicting customer behavior and how to use it when building customer models. It is done by conducting an empirical study across a wide range of experimental conditions. The experimental results show that context does matter when modeling the behavior of individual customers and that it is possible to infer the context from the existing data with reasonable accuracy in certain cases. It is also shown that significant performance improvements can be achieved if the context is "cleverly" modeled, as described in this paper. These findings have significant implications for data miners and marketers. They show that contextual information does matter in personalization and companies have different opportunities to both make context valuable for improving predictive performance of customers' behavior and decreasing the costs of gathering contextual information. <s> BIB002 </s> Mobile recommender systems: An overview of technologies and challenges <s> INTRODUCTION <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> BIB003 </s> Mobile recommender systems: An overview of technologies and challenges <s> INTRODUCTION <s> The rapid growth of mobile applications has imposed new threats to privacy: users often find it challenging to ensure that their privacy policies are consistent with the requirements of a diverse range of of mobile applications that access personal information under different contexts. This problem exacerbates when applications depend on each other and therefore share permissions to access resources in ways that are opaque to an end-user. To meet the needs of representing privacy requirements and of resolving dependencies issues in privacy policies, we pro-pose an extension to the P-RBAC model for reasoning about plausible scenarios that can exploit such weaknesses of mobile systems. This work has been evaluated using the case studies on several Android mobile applications. <s> BIB004 </s> Mobile recommender systems: An overview of technologies and challenges <s> INTRODUCTION <s> Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications. <s> BIB005 </s> Mobile recommender systems: An overview of technologies and challenges <s> INTRODUCTION <s> In many online applications, the range of content that is offered to users is so wide that a need for automated recommender systems arises. Such systems can provide a personalized selection of relevant items to users. In practice, this can help people find entertaining movies, boost sales through targeted advertisements, or help social network users meet new friends. To generate accurate personalized recommendations, recommender systems rely on detailed personal data on the preferences of users. Examples are ratings, consumption histories, and personal profiles. Recommender systems are useful, however the privacy risks associated to gathering and processing personal data are often underestimated or ignored. Many users are not sufficiently aware if and how much of their data is collected, if such data is sold to third parties, or how securely it is stored and for how long. This chapter aims to provide insight into privacy in recommender systems. First, we discuss different types of existing recommender systems. Second, we give an overview of the data that is used in recommender systems. Third, we examine the associated risks to data privacy. Fourth, relevant research areas for privacy-protection techniques and their applicability to recommender systems are discussed. Finally, we conclude with a discussion on applying and combining different privacy-protection techniques in real-world settings, making clear mappings to reflect typical relations between recommender system types, information types, particular privacy risks, and privacy-protection techniques. <s> BIB006 </s> Mobile recommender systems: An overview of technologies and challenges <s> INTRODUCTION <s> The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference. <s> BIB007
The evolution of computers in combination with the rapid development of mobile phone platforms such as the Google Android and the Apple iPhone in conjunction with the need for m-commerce need has brought up the use of personalization and recommendation systems. The use of the internet is moving forward with a direction towards mobile devices and although such devices come in various forms, in this paper we refer to smartphones that can access the internet and have features to enable access to information through a suitable way. Moreover, mobile devices have limitations related to hardware capabilities, network connections and usability issues such as the screen size BIB003 . Mobile devices have become very popular nowadays and they are considered to be a primary access information environment. However as the information on the internet grows and the people who use these devices become larger and larger there is a need to face the challenges that are tight related to mobile computing environments. The need to face the information overload is the most important nowadays in combination with the limitations that most of these devices have (such as the size of the screen, the processor speed and memory capabilities), direct us to the use of recommendation technologies. Recommender systems are concerned with the dynamic customization of data received over the World Wide Web and are based to user preferences. The scope of the recommendations is to assist the user to decide what to buy, who to make friend to a social network or what news to read. Due to information overload on the internet, personalization systems are one of the most valuable tools BIB007 . Additionally it should be noted that it is a very demanding process to design and develop such a system, since it combines knowledge and skills from different computer science fields. Despite of that, a number of well-respected methods have been developed the past few years, with some of them being used in commercial environments. Moreover, in mobile devices the information access problem becomes even harder because of the difficulties found. It is important to note that the algorithms applied to personal computers cannot be transferred directly to a mobile device, since there are different needs, special characteristics and limitations. The needs are about m-commerce, locationbased services found mainly in tourism and mobile financial services. Characteristics refer to the user interface, processor, memory capabilities and limitations are about the network boundaries found in GSM, Wi-Fi and GPS systems. However the advantages are more important. These include the ubiquity and the location-based service. These are the crucial factors that mobile recommender systems are based on most of the time. Furthermore the need for privacy has become a very important aspect of mobile phones that use personalization techniques. It is vital for the system to use some private data in order to provide accurate recommendations. However it should be taken into consideration that privacy is a massive problem with negativity towards the use of recommenders in mobile environments BIB001 BIB004 BIB006 . Most of the time simple users are not aware how e-commerce organizations use these data and they react in various destructive ways. We have reached a point that merchants want to improve their service and use unfair practices. However, there is a reconciliation point that could be reached if both parties are willing to work towards this road. The research is focused on how the users can receive personalized content in their smartphones using current technologies. However, in mobile environments attributes such as location and time should be embedded to such algorithms but on the other hand there are privacy concerns that have to be taken into consideration. Moreover, a mixture of current recommendation technologies that study the special mobile characteristics and use data from social networking websites will be discussed, along with technologies that can be used to solve the privacy problem. Additionally, there are still open questions that need extensive research to be answered. These include the goals of mobile recommender systems and the expectations of the users, if there are any type of implications associated with the location-based parameter and if this should be compulsory or enforced only when questioned. Also, it is not clear if there is a mobile domain or there is a number of e-commerce scenarios that is more suitable to mobile devices such as tourism. However these questions are still open, because mobile computing is a relatively new field of study in computer science and the lack of surveys and results is obvious BIB005 BIB003 . In addition to the above, an issue in mobile and ubiquitous computing is context awareness. By context is denoted the environment parameters and if they can play a role in recommendation results. The context usually adds additional parameters to the running algorithm which include demographics, objects, surroundings or other factors that could be considered relevant by the user such as health, mood, social activities and application context like web browser history and email mining. Also into consideration the sequence of visits can be taken in order to find a pattern and make predictions  BIB003 BIB002 . The application areas that widely use context awareness at the moment include, but are not limited to, m-commerce and tourism.