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The evolution of computers in combination with the rapid development of related networking infrastructures has brought e-commerce to a new level. The use of the Internet is moving forward and the need for e-commerce is becoming more wide and in different ways (Jannach et al., 2010). However as the information on the internet grows and the people who use these devices become larger there is a need to face the challenges that are tight related to these environments. The need to face the information overload is the most important nowadays and directs us to the use of recommendation technologies (Konstan & Riedl, 2012).
Recommender systems are concerned with the dynamic customization of data received over the World Wide Web and are based on user preferences (Ricci et al., 2011). 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 (Konstan & Riedl, 2012; Polatidis & Georgiadis, 2013, Prasad & Kumari 2012). Due to information overload on the internet, personalization systems are one of the most valuable tools nowadays. 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 (Konstan & Riedl, 2012; Ricci, 2011). 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 due to hardware limitations.
It is important to note that the algorithms applied to web based systems cannot be transferred directly to a mobile device, since there are different needs, characteristics and limitations. The needs are about location-based services found mainly in tourism and mobile financial services. Characteristics refer to the user interface, processing power, memory capabilities and limitations, which are about the network boundaries found in the Global System for Mobile Communications (GSM), Wi-Fi and the Global Positioning System (GPS). However the advantages are more important and include the ubiquity and the location-based service, which are crucial factors that mobile recommender systems are based (Ricci, 2011).
Furthermore the need for privacy has become a very important aspect of personalization techniques (Kobsa, 2007; Shyong et al., 2006; Benats et al., 2011; Jeckmans et al., 2013). 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 personalized environments (Jeckmans et al., 2013; Polatidis & Georgiadis, 2013). 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 aims to show that personalized systems can improve the user experience. 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 (Ricci, 2011).