loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.236.145.110

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
José de Borba, E.; Gasparini, I. and Lichtnow, D. (2017). The Use of Time Dimension in Recommender Systems for Learning. In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-248-6; ISSN 2184-4992, SciTePress, pages 600-609. DOI: 10.5220/0006312606000609

@conference{iceis17,
author={Eduardo {José de Borba}. and Isabela Gasparini. and Daniel Lichtnow.},
title={The Use of Time Dimension in Recommender Systems for Learning},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2017},
pages={600-609},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006312606000609},
isbn={978-989-758-248-6},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - The Use of Time Dimension in Recommender Systems for Learning
SN - 978-989-758-248-6
IS - 2184-4992
AU - José de Borba, E.
AU - Gasparini, I.
AU - Lichtnow, D.
PY - 2017
SP - 600
EP - 609
DO - 10.5220/0006312606000609
PB - SciTePress