Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area

Content dissemination in participatory delay tolerant networks

As experience with the Web 2.0 has demonstrated, users have evolved from being only consumers of digital content to producers. Powerful handheld devices have further pushed this trend, enabling users to consume rich media (for example, through high resolution displays), as well as create it on the go by means of peripherals such as built-in cameras. As a result, there is an enormous amount of user-generated content, most of which is relevant only within local communities. For example, students advertising events taking place around campus. For such scenarios, where producers and consumers of content belong to the same local community, networks spontaneously formed on top of colocated user devices can offer a valid platform for sharing and disseminating content. Recently, there has been much research in the field of content dissemination in mobile networks, most of which exploits user mobility prediction in order to deliver messages from the producer to the consumer, via spontaneously formed Delay Tolerant Networks (DTNs). Common to most protocols is the assumption that users are willing to participate in the content distribution network; however, because of the energy restrictions of handheld devices, users’ participation cannot be taken for granted. In this thesis, we design content dissemination protocols that leverage information about user mobility, as well as interest, in order to deliver content, while avoiding overwhelming noninterested users. We explicitly reason about battery consumption of mobile devices to model participation, and achieve fairness in terms of workload distribution. We introduce a dynamic priority scheduling framework, which enables the network to allocate the scarce energy resources available to support the delivery of the most desired messages. We evaluate this work extensively by means of simulation on a variety of real mobility traces and social networks, and draw a comparative evaluation with the major related works in the field

Customisable e-training programmes based on trainees profiles

Dissertation presented at Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa to obtain the Master degree in Electrical and Computer EngineeringOnline training (e-training) is a major driver to promote the development of competencies and knowledge in enterprises. A lack of customizable e-training programmes based on trainees‟ profiles and of continuous maintenance of the training materials prevents the sustainability of industrial training deployment. This dissertation presents a training strategy and a methodology for building training courses with the purpose to provide a trainee oriented industrial training development. The training strategy intends to facilitate the management of all the training components and tasks to be able to build a training structure focused in a specific planned objective. The methodology for building e-training courses proposes to create customizable training materials in an easier way, enabling various organizations to participate actively on its production. Additionally a customisable training programme framework is presented. It is supported by a compliant ontology-based model able to support adaptable training contents, orchestration service, facilitating the efficiency and acceptance of the e-training programmes delivery

Resources Review: Adaptive (podcast), Montreal*in/accessible (mobile app), Accessible Arcade Tables (DIY project)

Thisresourcesreview spotlights a variety of DIY (do-it yourself) innovative media projects. Examples of these projects include: a podcast series on adaptive technologies, building mobile applications that allow participants to publish images, text and sound recordings, an interactive map that documents disability discrimination in the certain cities and the DIY creation of accessible arcade tables. The social production of disability is seen in barriers created by society to restrict access to certain places or even certain cultural forms. The internet offers a space to share media productions and social media initiatives that use digital media to intervene, creatively, in theableistassumptions embedded in life both on- and offline

Exploiting relational tag expansion for dynamic user profile in a tag-aware ranking recommender system

A tag-aware recommender system (TRS) presents the challenge of tag sparsity in a user profile. Previous work focuses on expanding similar tags and does not link the tags with corresponding resources, therefore leading to a static user profile in the recommendation. In this article, we have proposed a new social tag expansion model (STEM) to generate a dynamic user profile to improve the recommendation performance. Instead of simply including most relevant tags, the new model focuses on the completeness of a user profile through expanding tags by exploiting their relations and includes a sufficient set of tags to alleviate the tag sparsity problem. The novel STEM-based TRS contains three operations: (1) Tag cloud generation discovers potentially relevant tags in an application domain; (2) Tag expansion finds a sufficient set of tags upon original tags; and (3) User profile refactoring builds a dynamic user profile and determines the weights of the extended tags in the profile. We analysed the STEM property in terms of recommendation accuracy and demonstrated its performance through extensive experiments over multiple datasets. The analysis and experimental results showed that the new STEM technique was able to correctly find a sufficient set of tags and to improve the recommendation accuracy by solving the tag sparsity problem. At this point, this technique has consistently outperformed state-of-art tag-aware recommendation methods in these extensive experiments

Web 2.0 technologies for learning: the current landscape – opportunities, challenges and tensions

This is the first report from research commissioned by Becta into Web 2.0 technologies for learning at Key Stages 3 and 4. This report describes findings from an additional literature review of the then current landscape concerning learner use of Web 2.0 technologies and the implications for teachers, schools, local authorities and policy makers