Exploiting Linked Data-based Personalization Strategies for Recommender Systems

Gabriela Da Silva, Gabriela Da Silva, Lara do Nascimento, Frederico Durão

2022

Abstract

People seek assertive and reliable recommendations to support their daily decision-making tasks. To this end, recommendation systems rely on personalized user models to suggest items to a user. Linked Data-driven systems are a kind of Web Intelligent systems, which leverage the semantics of links between resources in the RDF graph to provide metadata (properties) for the user modeling process. One problem with this approach is the sparsity of the user-item matrix, caused by the many different properties of an item. However, feature selection techniques have been applied to minimize the problem. In this paper, we perform a feature selection preprocessing step based on the ontology summary data. Additionally, we combine a personalization strategy that associates weights with relevant properties according to the user’s previous interactions with the system. These strategies together aim to improve the performance and accuracy of the recommender system, since only latent representations are processed by the recommendation engine. We perform several experiments on two publicly available datasets in the film and music domains. We compare the advantages and disadvantages of the proposed strategies with non-personalized and non-preprocessed approaches. The experiments show significant increases in top-n recommendation tasks in Precision@K (K=5, 10), Map, and NDCG.

Paper Citation

in Harvard Style

Da Silva G., do Nascimento L. and Durão F. (2022). Exploiting Linked Data-based Personalization Strategies for Recommender Systems. In Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-613-2, pages 226-237. DOI: 10.5220/0011591300003318

in Bibtex Style

@conference{webist22,
author={Gabriela Da Silva and Lara do Nascimento and Frederico Durão},
title={Exploiting Linked Data-based Personalization Strategies for Recommender Systems},
booktitle={Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2022},
pages={226-237},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011591300003318},
isbn={978-989-758-613-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Exploiting Linked Data-based Personalization Strategies for Recommender Systems
SN - 978-989-758-613-2
AU - Da Silva G.
AU - do Nascimento L.
AU - Durão F.
PY - 2022
SP - 226
EP - 237
DO - 10.5220/0011591300003318