Mobile Multimedia Recommendation in Smart Communities: A Survey <s> A. Recommendation in Mobile Social Learning <s> Abstract Given the growing use of mobile devices, there is now increasing interest in the potential for supporting the mobile learner. However, there remains much research to be undertaken, to find effective ways of facilitating learning with mobile devices. This paper considers how to support the mobile language learner using a handheld computer. It introduces TenseITS, a language learning environment that adapts the interaction to the individual learner’s understanding, as represented in a learner model constructed during the interaction. It also adapts according to contextual features of the learner’s location that may affect their ability to study – the likelihood that they will be interrupted by others; their general ability to concentrate at that location; and the amount of time they have available for study. <s> BIB001 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> A. Recommendation in Mobile Social Learning <s> A learning context describes the current situation of a learner related to a learning activity, and continually changes in a mobile learning environment. As a result, context-awareness in mobile learning has become an essential part when designing more adaptive mobile learning systems. In this paper, we introduce our novel Context- aware and adaptive learning schedule framework which makes use of a learning schedule to support the students' daily routines, adapts the activities to the student's learning styles and then selects the appropriate activity for the learner based on their current learning context. <s> BIB002 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> A. Recommendation in Mobile Social Learning <s> Personalization services in a ubiquitous computing environment-ubiquitous personalization services computing-are expected to emerge in diverse environments. Ubiquitous personalization must address limited computational power of personal devices and potential privacy issues. Such characteristics require managing and maintaining a client-side recommendation model for ubiquitous personalization. To implement the client-side recommendation model, this paper proposes buying-net, a customer network in ubiquitous shopping spaces. Buying-net is operated in a community, called the buying-net space, of devices, customers, and services that cooperate together to achieve common goals. The customers connect to the buying-net space using their own devices that contain software performing tasks of learning the customers' preferences, searching for similar customers for network formation, and generating recommendation lists of items. Buying-net attempts to improve recommendation accuracy with less computational time by focusing on local relationship of customers and newly obtained information. We experimented with such customer networks in the area of multimedia content recommendation and validated that buying-net outperformed a typical collaborative-filtering-based recommender system on accuracy as well as computational time. This shows that buying-net has good potential to be a system for ubiquitous shopping. <s> BIB003 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> A. Recommendation in Mobile Social Learning <s> Mobile devices are becoming a primary medium for personal information gathering, management, and sharing. Managing personal image data on mobile platforms is a difficult problem due to large data set size, content diversity, heterogeneous individual usage patterns, and resource constraints. This article presents a user-centric system, called iScope, for personal image management and sharing on mobile devices. iScope uses multi-modality clustering of both content and context information for efficient image management and search, and online learning techniques for predicting images of interest. It also supports distributed content-based search among networked devices while maintaining the same intuitive interface, enabling efficient information sharing among people. We have implemented iScope and conducted in-field experiments using networked Nokia N810 portable Internet tablets. Energy efficiency was a primary design focus during the design and implementation of the iScope search algorithms. Experimental results indicate that iScope improves search time and search energy by 4.1X and 3.8X on average, relative to browsing. <s> BIB004 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> A. Recommendation in Mobile Social Learning <s> This paper presents a solution for recommending documents to students according to their current activity that is tracked in terms of semantic annotations associated to the accessed resources. Our approach is based on an existing tracking system that captures the user current activity, which is extended to build a user profile that comprises his/her interests in term of ontological concepts. A recommendation service is elaborated, implementing an algorithm that is alimented by Contextualized Attention Metadata (CAM) comprising the annotation of documents accessed by learners. The user profile is updated as soon as an activity is completed; thus, recommendations provided by the service are up-to-date in real time. The original aspect of this recommendation approach consists in combining a user activity tracking system with the exploitation of the semantic annotations associated with resources. <s> BIB005 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> A. Recommendation in Mobile Social Learning <s> According to studies into learning at work, interpersonal help seeking is the most important strategy of how people acquire knowledge at their workplaces. Finding knowledgeable persons, however, can often be difficult for several reasons. Expert finding systems can support the process of identifying knowledgeable colleagues thus facilitating communication and collaboration within an organization. In order to provide the expert finding functionality, an underlying user model is needed that represents the characteristics of each individual user. In our article we discuss requirements for user models for the workintegrated learning (WIL) situation. Then, we present the APOSDLE People Recommender Service which is based on an underlying domain model, and on the APOSDLE User Model. We describe the APOSDLE People Recommender Service on the basis of the Intuitive Domain Model of expert finding systems, and explain how this service can support interpersonal help seeking at workplaces. <s> BIB006 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> A. Recommendation in Mobile Social Learning <s> 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. <s> BIB007 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> A. Recommendation in Mobile Social Learning <s> In this paper, we propose a system based on hypothesis that when we ask for help from others, 1) the closer people are, the more easy it is to get help, 2) the more simple things are easier to get help with. We carried out a survey to examine the hypothesis. The results show that this hypothesis is correct. Based on this hypothesis, we proposed a social networking service site based on a mobile environment called SENSMILE, which supports learners to find a partner who can solve their problems at the online community, and an appropriate request chain of friends will be recommended upon their request by utilizing personal relationships. The system also supports for collaborative learning by using location based sensing information. <s> BIB008 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> A. Recommendation in Mobile Social Learning <s> Facebook is currently the most popular social networking site, and it has been used in a number of collaborative learning applications. In a collaborative learning environment, an adaptive learning companion could help learners understand the learning content and share knowledge, as well as increase creativity and facilitate the beneficial effects of collaborative learning. In this study, a learning companion recommendation system (LCRS) is developed on Facebook, which supports mobile collaborative learning. The system collects friends' profile data automatically according to their learning needs, such as interests and professional abilities. Moreover, the technology acceptance model and partial least squares regression are used to investigate learners' acceptance of the LCRS for learning activities <s> BIB009
Recommender systems in the mobile social learning and Technology Enhanced Learning (TEL) have been investigated extensively by researchers during the last decade BIB007 [65]- BIB005 . When suitable resources from a potentially overwhelming variety of choices are identified, recommender systems offer an encouraging approach to facilitate both learning and teaching tasks. As learning is taking place in exceptionally diverse and rich environments, the incorporation of contextual information about the learner in the recommendation process has attracted major interests among researchers. Such contextualization is currently being researched as a paradigm for building intelligent systems that can better predict the needs of users (learners), and act more efficiently in response to their behavior. It is difficult to express a specific learning requirement through keywords. Search engines such as Google do a reduced job when a learner needs content about "relativity theory", oriented to high school level, with a duration of about 40 minutes. Finding relevant resources can be even more difficult when requirements are not always fully known by the learner, such as his/her level of competence or adequate technical format. Recommender systems for learning try to address these challenges -i.e. they attempt to filter content for different learning settings. Apart from using learner interest and available multimedia learning resources as a basis to generate recommendations in mobile social learning, the notion of context has started to attract significant research attention. A learning context describes the current situation of a learner related to a learning activity, and continually changes in a mobile learning environment. As a result, context-awareness in mobile learning has become an essential part when designing more adaptive mobile learning systems. A new set of recommender systems for learning have been developed in recent years to demonstrate the potential of contextual recommendation. A first example of a context-aware recommender system for learning considers the physical conditions, location of the user and the noise level at this location as a basis to suggest and recommend learning resources to the learner. Beham et al. BIB006 discussed and addressed the requirements for user models for a Work Integrated Learning (WIL) situation. They presented the APOSDLE people recommender service which is based on an underlying domain model, and on the APOSDLE user model. The APOSDLE People Recommender Service works on the basis of the Intuitive Domain Model of expert finding systems, and explain to support interpersonal help seeking at workplaces. Broisin et al. BIB005 used location and tracking as context and presented a solution for recommending documents to students according to their current activity that is tracked in terms of semantic annotations associated to the accessed resources. Their approach was based on an existing tracking system that captures the user's current activity, which is extended to build a user profile that comprises his/her interests in term of ontological concepts. A recommendation service was elaborated by implementing an algorithm that is alimented by Contextualized Attention Metadata (CAM) comprising the annotation of documents accessed by learners. In the same vein, Yin et al. BIB008 proposed a system to address and contribute to the challenge of location in a mobile social learning environment based on hypothesis that involves asking for help from others. These hypotheses were: 1) the closer people are, the easier it is to get help; 2) the more simple things are, the more it is easier to get help with them. They carried out a survey to examine the above hypotheses. The results showed that these hypotheses are correct. Based on these hypotheses, they proposed a social networking service based on a mobile environment called SENSMILE, which supports learners to find a partner who can solve their problems at the online community, and an appropriate request chain of friends will be recommended upon their request by utilizing personal relationships. The system also supports collaborative learning by using location based sensing information. Chen et al. BIB009 developed a Learning Companion Recommendation System (LCRS) on Facebook. LCRS supports a mobile collaborative learning by automatically collecting profile data such as interests and professional abilities of friends', in accordance to their learning needs. Furthermore, the technology acceptance model and partial least squares regression are used to investigate learners' acceptance of the LCRS for learning activities. To implement the client-side recommendation model, Kim et al. BIB003 proposed "buying-net", a customer network in ubiquitous shopping spaces. Buying-net is operated in a community, called the buying-net space, of devices, customers, and services that cooperate together to achieve common goals. The customers connect to the buying-net space using their own devices that contain software performing tasks of learning the customers' preferences, searching for similar customers for network formation, and generating recommendation lists of items. Buying-net attempts to improve recommendation accuracy with less computational time by focusing on local relationship of customers and newly obtained information. Zhu et al. BIB004 presented a user-centric system, called iScope, for personal image management sharing on mobile devices. The iScope system uses multimodality clustering of both content and context information for efficient image management and search, and online techniques for predicting images of interest. iScope also supports distributed content-based search among networked devices while maintaining the same intuitive interface, enabling efficient information sharing among people. Cui and Bull BIB001 discussed how to support the mobile language learner using a handheld computer. They introduced TenseITS, a language learning environment that adapts the interaction to the individual learner's understanding, as represented in a learner model constructed during the interaction. TenseITS also adapts according to contextual features of the learner's location that may affect their ability to study. Yau and Joy BIB002 also introduced a contextaware and adaptive learning schedule framework which makes use of a learning schedule to support the students' daily routines, adapts the activities to the student's learning styles and then selects the appropriate activity for the learner based on their current learning context.