You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Mobile Multimedia Recommendation in Smart Communities: A Survey 
Chapter Title:  C. Context-Aware Services 
1. Abstract of Cited Paper (BIB001):  In this paper we present a suggestion for a next generation mobile GIS architecture. The proposed architecture enables to use GIS data also in mobile devices and to handle information received from local sensors. The architecture has been used to design CENTRIA Locawe platform, which has been tested in several field experiments. This platform has been used in various industrial pilots and prototypes, which have proven that Locawe platform can be used efficiently in developing location-aware services for both in outdoor as well as indoor environments. 
2. Abstract of Cited Paper (BIB002):  Modern museums provide visitors with mobile, context-aware information and guidance systems. Unfortunately, these systems often suffer from several technological drawbacks: expensive infrastructure, complex maintenance and content modification, difficult to learn user interfaces, manual location specification requirements, etc. Visitors have to rent, learn, carry and return yet another mobile device. On the other hand, most modern nomadics already own high-performance mobile devices: smart-phones. But acceptance to install museum software on a personal device is rather limited. In this paper, a novel mobile, context-aware museum information system is introduced that runs on the visitors' mobile phones without any prior installation. We outline the technical principles of the underlying Medient infrastructure and present HolstenTour as an example service instantiation for the famous Museum Holstentor in Luebeck, Germany. 
3. Abstract of Cited Paper (BIB003):  In this paper, we describe ROSE (Routing Service), a mobile application which combines pedestrian navigation with event recommendation and live public transport support. It determines the best possible transport link and accompanies passengers throughout their entire journey. Our motivation is to free the passenger from as many tedious tasks as possible in finding an interesting event and on the way to it. Further, it reacts in real time to delays in the public transport system and calculates alternative routes when necessary. For route planning in this context, we will propose a two step algorithm for incorporating non monotone and non optimistic multi dimensional user preferences in an A*-like algorithm. We also present an assignment of theoretical foundations to real world route planning problems. 
4. Abstract of Cited Paper (BIB004):  Mobile and Ubiquitous computing have increased the need for mobile users to expect accessing preferred services, whenever they want and wherever they are. Users do not have to explicitly specify and configure their working environment each time they move from one location to another. The necessary adaptation to cope with the changing environment should be initiated by services rather than by users. This paper presents a context-aware platform for supporting mobile users with personalized services. The platform is capable of handling different types of context sources (i.e. sensors, readers, agents), offers sophisticated mechanisms in matching the mobile user's preferences with services that are enabled at the visited location, and provides these services in personalized and adaptive manner to the user conditions. As a proof of concept, we present an context sensitive instant messaging prototype where a user is provided with services and information of interest based on location and time. The platform monitors a set of policies to dynamically adapt each application behavior to the situation of its use. 
5. Abstract of Cited Paper (BIB005):  We present information extraction for a semantic personalised tourist recommender system. The main challenges in this setting are that information is spread across various information sources, it is usually stored in proprietary formats and is available in different languages in varying degrees of accuracy. We address the mentioned challenges and describe our realization and ideas how to deal with each of them. In our paper we describe scraping and extracting keywords from different web portals with different languages, how we deal with missing multi-lingual data, and how we identify the same objects from different sources.

Literature Review:
In contrast to non-mobile situations such as desktop computer use, mobile computing has brought the notion that the physical and logical context of users or participants from the smart community can power the performance of services they demand for. A fascinating aspect of computing with mobile devices is the decomposition of task from the location and situation where it is performed in the community  BIB002 . Users can network with coworkers in real-time from home, Figure 2 . A mobile social learning smart community in meetings, conferences and tradeshows, at the airport, in the office, in the supermarket etc. In broader terms, context may refer to any aspect of circumstances where an entity (person, place, or computing object) may invoke computing functions. With current mobile applications, functions would most likely to be specified as demands for service from either local or distant service providers. In this kind of condition, the mobile user and his/her device becomes the service consumer. The digital representation of contextual data can avoid the need to track down the information in other ways. For instance, a location-aware museum guide is more effective than a pamphlet one must search through. An automatically recorded meeting eliminates the need to take exhaustive notes or ask someone who attended. Such familiar situations point to the great interrelatedness of people, events and the compactness of the contexts in which we function. As depicted in Figure 3 , any given context may comprise information about the physical world (e.g., location, movement, device characteristics, etc.) and about the logical domain neighboring the service consumer. The logical world contains information about personality, preferences and relationships in different domains, such as friends, work, family and others. Even historic information about any of these features might be incorporated. For example, home address might be contained in the service consumer's logical context. Context data about the physical world can be collected in real-time from sensors available in a mobile device. But the necessary information about the logical world is collected directly from the user/participant or gathered from interactions and communications the user has made with service providers  . Whatever the nature of such information, context may come from different ways and has a relatively temporary lifetime. Context-aware smart systems have been studied in various services, for instance, event navigation BIB004 , tourist guidance BIB006 BIB003 , and context-aware messaging BIB001 - BIB005 .