Linking it all June 4, 2008
Posted by estrella in Uncategorized.add a comment
To finish with this topic, today I’ll present the solution proposed by Rey et al., a mix of what I’ve already talked about and more…
First, the user receives the subject’s educative contents and ontology (conforming ADL SCORM) from the contents provider, and the DiTV programs (conforming TV-Anytime).
As you could read previously, the programs should be tagged by the viewers, improving in this way the semantic tagging. This means that the user should be given an interface in which they could write the tags. The developed interface is seen over the program screen and it offers some facilities as suggestions to avoid writing the whole word (we must keep in mind that the user has only the TV remote control to manage the interface), previous assigned tags and popular tags for that program, as you can see in this screenshot from the article:
Now, the big question is: how can the system use the tags to provide programs related to a topic?
As the number of repetitions in global assigned tags grows, the relationships among them are computed, and also the relationships among the programs assigned popular tags. By means of these relationships, a main subset of programs can be built up, being the relevant tags those appearing in the ontology. Related programs can be added to this set, if they have been assigned some (though not all) of the relevant tags, enhacing the experience of the user. Popular tags are also sent to the provider, so that they can be delivered together with the program.
As the computation of relationships is too heavy for a simple device as a set-top box, it is done in a remote server, leaving for the device the only task of filtering the programs according to the set of relevant tags.
This server + local device system runs AVATAR, and you can read more about it in the International Journal of Pattern Recognition and Artificial Intelligence, Special Issue on Personalization Techniques for Recommender Systems and Intelligen User Interfaces, 21(2), pages from 397 to 422.
So… that’s all! I hope you enjoyed the reading and learned a bit about t-learning and this proposal :)
