Authors:
Eduardo José de Borba
1
;
Isabela Gasparini
1
and
Daniel Lichtnow
2
Affiliations:
1
Santa Catarina State University (UDESC), Brazil
;
2
Federal University of Santa Maria (UFSM), Brazil
Keyword(s):
Recommender System, Context-aware, Time, Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computer-Supported Education
;
e-Learning
;
e-Learning and e-Teaching
;
Enterprise Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Software Agents and Internet Computing
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
When the amount of learning objects is huge, especially in the e-learning context, users could suffer cognitive overload. That way, users cannot find useful items and might feel lost in the environment. Recommender systems are tools that suggest items to users that best match their interests and needs. However, traditional recommender systems are not enough for learning, because this domain needs more personalization for each user profile and context. For this purpose, this work investigates Time-Aware Recommender Systems (Context-aware Recommender Systems that uses time dimension) for learning. Based on a set of categories (defined in previous works) of how time is used in Recommender Systems regardless of their domain, scenarios were defined that help illustrate and explain how each category could be applied in learning domain. As a result, a Recommender System for learning is proposed. It combines Content-Based and Collaborative Filtering approaches in a Hybrid algorithm that cons
iders time in Pre-Filtering and Post-Filtering phases.
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