Meta-rules: Improving adaptation in recommendation systems
Autor:
Romero Zaldivar, Vicente Arturo
; Burgos, Daniel
Fecha:
2010Palabra clave:
Revista / editorial:
Proceedings ABIS 2010 - 18th Intl. Workshop on Personalization and Recommendation on the Web and BeyondTipo de Ítem:
conferenceObjectResumen:
Recommendation Systems are central in current applications to help the user find useful informat ion spread in large amounts of post. videos or social networks. Most Reconunendation Systems are more effective when huge amounts of user data are available in order to calculate similarit ies between users. Educational applications are not popular enough in order to generate large amount of data. In this context, nile-based Reco tumendarion Systems are a better solution. Rules are in most cases written a priori by dom ain experts: they can offer good recomiuendat ions with even no application of usage informat ion. However large rule-sets are hard to maint ain. reengineer and adapt to user goals and pref erences. Meta-rules. rules that generate rules, can generalize a rule-set providing bases for adaptation. reengineering and on the fly generat ion. In this paper. the authors expose the benef its of meta-rules implemented as part of a metar ule based Recommendation System. This is an effective solution to provide a personalized reco mmendation to the learner, and constitutes a new approach in rule-based Recommendation Systems.
Descripción:
Ponencia de la conferencia "18th Intl. Workshop on Personalization and Recommendation on the Web and Beyond, ABIS 2010; Kassel; Germany; 4 October 2010 through 6 October 2010"
Este ítem aparece en la(s) siguiente(s) colección(es)
Estadísticas de uso
Año |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
2021 |
2022 |
2023 |
2024 |
Vistas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
29 |
25 |
26 |
54 |
59 |
Descargas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
Ítems relacionados
Mostrando ítems relacionados por Título, autor o materia.
-
Meta-Mender: A meta-rule based recommendation system for educational applications
Romero Zaldivar, Vicente Arturo; Burgos, Daniel (Procedia Computer Science, 2010)Recommenders are central in current applications to help the user find useful information spread in large amounts of data. Most Recommenders are more effective when huge amounts of user data are available in order to ... -
Automatic Discovery of Complementary Learning Resources
Romero Zaldivar, Vicente Arturo; Crespo García, Raquel M; Burgos, Daniel ; Delgado Kloos, Carlos; Pardo, Abelardo (Towards Ubiquitous Learning. EC-TEL 2011, 2011)Students in a learning experience can be seen as a community working simultaneously (and in some cases collaboratively) in a set of activities. During these working sessions, students carry out numerous actions that affect ... -
Meta-rule based recommender systems for educational applications
Romero Zaldivar, Vicente Arturo; Burgos, Daniel ; Pardo, Abelardo (IGI GlobalEducational Recommender Systems and Technologies: Practices and Challenges, 2011)Recommendation Systems are central in current applications to help the user find relevant information spread in large amounts of data. Most Recommendation Systems are more effective when huge amounts of user data are ...