PEERS ONLINE INTERACTION FRAMEWORK

4 verschillende metrics worden behandeld; Link Analysis Ranking, Similarity Recommendation, Personalized Ranking, Contextual Recommendation.

The tools used to search and find Learning Objects in different systems do not provide a meaningful and scalable way to rank or recommend learning material. This work propose and detail the use of Contextual Attention Metadata, gathered from the different tools used in the lifecycle of the Learning Object, to create ranking and recommending metrics to improve the user experience. Four types of metrics are detailed: Link Analysis Ranking, Similarity Recommendation, Personalized Ranking and Contextual Recommendation. While designed for Learning Objects, it is shown that these metrics could also be applied to rank and recommend other types of reusable components like software libraries.

another article about attention profiling

nog een artikel over attention metadata

article about APML and attention metadata

belangrijke ontwikkelingen (metadata standaarden etc.) voor het verbeteren van het aanbieden van content door het meten en gebruiken van context.

Accessing semantic data using anyMeta

de aanpak van mediamatic om data te retrieven uit andere systemen.

microblogging is errrug handig voor business.

more effective twittering with twalala.com. interesting blogpost.

Enterprise twitter services

very interesting discussion about the semantic web, why it will or won't fail, some historical points and standards being used.

discussion and networking tool for researchers..

abstract of the international conference april 27-29, 2007 @ mit about "Collaboration and Collective Intelligence". some interesting resources and issues.

Congres sociale netwerken, Rotterdam

interessant artikeltje over Twitter en de invloed van real-life friends in je netwerk.

In conclusion, even when using a very weak definition of "friend" (i.e., anyone who a user has directed a post to at least twice) we find that Twitter users have a very small number of friends compared to the number of followers and followees they declare. This implies the existence of two different networks: a very dense one made up of followers and followees, and a sparser and simpler network of actual friends. The latter proves to be a more influential network in driving Twitter usage since users with many actual friends tend to post more updates than users with few actual friends. On the other hand, users with many followers or followees post updates more infrequently than those with few followers or followees.

wat heeft dit te maken met PEERS?

wat heeft dit te maken met PEERS?

handig: lijst met mogelijke partners/concurrenten

list of white label social networking and community platforms.