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:  6) Demographic Recommender Systems 
1. Abstract of Cited Paper (BIB001):  As the World Wide Web becomes a large source of digital music, the music recommendation system has got a great demand. There are several music recommendation systems for both commercial and academic areas, which deal with the user preference as fixed. However, since the music preferred by a user may change depending on the contexts, the conventional systems have inherent problems. This paper proposes a context-aware music recommendation system (CA-MRS) that exploits the fuzzy system, Bayesian networks and the utility theory in order to recommend appropriate music with respect to the context. We have analyzed the recommendation process and performed a subjective test to show the usefulness of the proposed system. 
2. Abstract of Cited Paper (BIB002):  Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths. 
3. Abstract of Cited Paper (BIB003):  To meet more and more complex recommendation needs, it is quite important to implement hybrid recommendations for mobile commerce. In this paper, we propose a design for open hybrid recommendation systems in mobile commerce, which could integrate multiple recommendation algorithms together to improve recommendation performance. First, three solutions for an open hybrid recommendation approach are discussed in detail, which are generic customer profile, weighted hybrid recommendation algorithm, and mobile device profile creation. After that, we give out a multi-agent architecture design to make the three solutions work together. Finally a prototype system based on our proposed architecture is implemented to demonstrate the feasibility of our design and evaluate the performance of the proposed open hybrid recommendation system. 
4. Abstract of Cited Paper (BIB004):  The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research. 
5. Abstract of Cited Paper (BIB005):  Recommender systems support internet users in the often awkward task of finding suitable products in a vast and/or complex product assortment. Many different types of recommenders have been developed during the last decade. From a technical point of view those approaches already work well. What has been widely neglected are decision theoretical phenomenons which can severely impact on the optimally of the taken decision as well as on the challenge to take a decision at all. This paper deals with decoy effects, which have already shown big persuasive potential in marketing and related fields. The big question to be answered in this paper is how to automatically calculate decoy effects in order to identify unforeseen side effects. This includes the presentation of a new decoy model, its combination with utility values calculated by a recommender system, an empirical evaluation of the model, and a corresponding user interface, which serves as starting point for controlling and implementing decoy effects in recommender systems. 
6. Abstract of Cited Paper (BIB006):  Mobile-enabled social networks applications are becoming increasingly popular. Most of the current social network applications have been designed for high-end mobile devices, and they rely upon features such as GPS, capabilities of the world wide web, and rich media support. However, a significant fraction of mobile user base, especially in the developing world, own low-end devices that are only capable of voice and short text messages (SMS). In this context, a natural question is whether one can design meaningful social network-based applications that can work well with these simple devices, and if so, what the real challenges are. Towards answering these questions, this paper presents a social network-based recommender system that has been explicitly designed to work even with devices that just support phone calls and SMS. Our design of the social network based recommender system incorporates three features that complement each other to derive highly targeted ads. First, we analyze information such as customer's address books to estimate the level of social affinity among various users. This social affinity information is used to identify the recommendations to be sent to an individual user. Second, we combine the social affinity information with the spatio-temporal context of users and historical responses of the user to further refine the set of recommendations and to decide when a recommendation would be sent. Third, social affinity computation and spatio-temporal contextual association are continuously tuned through user feedback. We outline the challenges in building such a system, and outline approaches to deal with such challenges. 
7. Abstract of Cited Paper (BIB007):  Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.

Literature Review:
Demographic recommender systems aim to categorize the user based on personal attributes and make recommendations based on demographic classes such as gender and age. For example, the evaluation procedure used in BIB004 , utilized a demographic recommendation algorithm as part of the integration of three other recommendation algorithms to provide a hybrid recommendation as a prototype. Table II shows a comparison of traditional recommender systems.Figure5depicts a typical architecture of a mobile multimedia recommender system involving collaborative filtering, content-based filtering and hybrid recommender systems. The user model and user's mobile device are all connected to a server through an interactive cloud network. To generate Content-Based Recommendations (CBR) the user's model which comprises his/her rating history and profile is matched with the multimedia information stored in the server through a Content-Based Filtering (CBF) algorithm. To generate Collaborative Recommendations (CR) the user is recommended multimedia information from the server based on matching between a user's rating history and other user's rating history with similar interests through a CF algorithm. In the Recommendation Agent CBR is combined with CR to generate a Hybrid Recommendation (HR) and thus final multimedia recommendations. The user's Personal Content Manager (PCM) in his/her mobile device manages his/her final multimedia recommendations in the Client Agent which is connected to the Recommendation Agent. Besides these categories, one special and upcoming type of recommender systems worth noting is Context-Aware Recommender Systems (CARS). Since the general concept of context is very broad, Adomavicius and Tuzhilin BIB005 noted that when applying CARS there should be a focus on fields such as data mining, e-commerce personalization, databases, information retrieval, ubiquitous and mobile context-aware Utility-Based BIB006 Can feature non-product attributes, such as vendor reliability and product availability. Creating a utility function for each user. Needs to know the utility functions of the user in order to generate a user profile Makes suggestions based on a computation of the utility of each object for the user. Demographic BIB004 BIB003 Not likely to require a history of user ratings as required by collaborative and content-based techniques. Demographic information in a user model can vary greatly, because of different types of systems which require recommendations. Needs to know personal attributes and demographic classes of users. Aim to categorize the user based on personal attributes and make recommendations based on demographic classes such as gender and age. systems, marketing and management which are directly related to recommender systems. Traditionally, recommender systems deal with applications having only two types of entities, users and items, and do not put them into contextual information, such as time, location or the company of other people. Examples of applicable contextual information for recommendation, include context-aware recommender systems that suggest queries for a given partial query  , contextualized media delivery systems BIB007 [51] BIB002 and intelligent tutorial guides BIB008 . In many applications, such as recommending a trip package, personalized content on a web site, or a movie/TV/video, it may however not be sufficient to consider only users and items -it is also important to incorporate the contextual information into the recommendation process in order to recommend items to users in circumstances such as ubiquity and mobility BIB005 [53]- BIB001 .