Workshop on recommender systems for technology-enhanced learning, Barcelona, Spain
29 September 2010 - 29 September 2010
Spain
The Workshop on recommender systems for technology-enhanced learning will be held on 29 and 30 September 2010 in Barcelona, Spain.
Recommender systems often use publicly available datasets from different application environments in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and compare them to other algorithms in given settings.
In such data sets, a representation of implicit or explicit feedback from the users regarding the candidate items is stored, in order to allow the recommender system to produce a recommendation. This feedback can be in several forms. For example, in the case of collaborative filtering systems it can be ratings or votes (i.e. if an item has been viewed or bookmarked). In the case of content-based recommenders, it can be product reviews or simple tags (keywords) that users provide for items. Additional information is also required such a unique way to identify who provides this feedback (user ID) and upon which item (item ID). The user-rating matrix used in collaborative filtering systems is a well-known example.
Although recommender systems are increasingly applied in technology-enhanced learning (TEL), it is still an application area that lacks data sets that would allow the experimental evaluation of the performance of different recommendation algorithms using comparable, interoperable, and reusable data sets.
For further information, please visit:
http://adenu.ia.uned.es/workshops/recsystel2010/datatel.htm(opens in new window)
Recommender systems often use publicly available datasets from different application environments in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and compare them to other algorithms in given settings.
In such data sets, a representation of implicit or explicit feedback from the users regarding the candidate items is stored, in order to allow the recommender system to produce a recommendation. This feedback can be in several forms. For example, in the case of collaborative filtering systems it can be ratings or votes (i.e. if an item has been viewed or bookmarked). In the case of content-based recommenders, it can be product reviews or simple tags (keywords) that users provide for items. Additional information is also required such a unique way to identify who provides this feedback (user ID) and upon which item (item ID). The user-rating matrix used in collaborative filtering systems is a well-known example.
Although recommender systems are increasingly applied in technology-enhanced learning (TEL), it is still an application area that lacks data sets that would allow the experimental evaluation of the performance of different recommendation algorithms using comparable, interoperable, and reusable data sets.
For further information, please visit:
http://adenu.ia.uned.es/workshops/recsystel2010/datatel.htm(opens in new window)