Refine
Document Type
- Conference Proceeding (2) (remove)
Language
- English (2)
Has Fulltext
- no (2)
Is part of the Bibliography
- no (2)
Institute
Mobile devices, in the form of smartphones, are endowed with rich capabilities in terms of multimedia, sensors and connectivity. The wide adoption of these devices allows using them across different settings and situations. One area in which mobile devices become more and more prominent is within the field of mobile learning. Here, mobile devices provide rich possibilities for the contextualization of the learner, by using the set of sensors available in the device. On the one hand, the usage of mobile devices enables participation in learning activities independent of time and space. Nevertheless, developing mobile learning applications for the heterogeneity of mobile devices available in the market becomes a challenge. Not only this is a problem related to form factor aspects, but also the large number of different operating systems, platforms and app infrastructures (app stores) are aspects to be considered. In this paper we present our initial efforts with regard to the development of cross-platform mobile applications to support the contextualization of learning content.
In this paper we present an approach for People-to-People recommendations based on a Rich Context Model (RCM). We consider personal user information as contextual information used for our recommendations. The evaluation of our recommendation approach was performed on a social network of students. The obtained results do show a significant increase in performance while, at the same time, a slight increase in quality in comparison to a manual matching process. The proposed approach is flexible enough to handle different data types of contextual information and easy adaptable to other recommendation domains.