A Multicriteria Evaluation of Hybrid Recommender Systems: On the Usefulness of Input Data Characteristics

Reinaldo Silva Fortes, Alan R. R. de Freitas, Marcos André Gonçalves

2017

Abstract

Recommender Systems (RS) may behave differently depending on the characteristics of the input data, encouraging the development of Hybrid Filtering (HF). There are few works in the literature that explicitly characterize aspects of the input data and how they can lead to better HF solutions. Such work is limited to the scope of combination of Collaborative Filtering (CF) solutions, using only rating prediction accuracy as an evaluation criterion. However, it is known that RS also need to consider other evaluation criteria, such as novelty and diversity, and that HF involving more than one approach can lead to more effective solutions. In this work, we begin to explore this under-investigated area, by evaluating different HF strategies involving CF and Content-Based (CB) approaches, using a variety of data characteristics as extra input data, as well as different evaluation criteria. We found that the use of data characteristics in HF proved to be useful when considering different evaluation criteria. This occurs in spite of the fact that the experimented methods aim at minimizing only the rating prediction errors, without considering other criteria.

Paper Citation

in Harvard Style

Silva Fortes R., R. R. de Freitas A. and André Gonçalves M. (2017). A Multicriteria Evaluation of Hybrid Recommender Systems: On the Usefulness of Input Data Characteristics . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-248-6, pages 623-633. DOI: 10.5220/0006315406230633

in Bibtex Style

@conference{iceis17,
author={Reinaldo Silva Fortes and Alan R. R. de Freitas and Marcos André Gonçalves},
title={A Multicriteria Evaluation of Hybrid Recommender Systems: On the Usefulness of Input Data Characteristics},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2017},
pages={623-633},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006315406230633},
isbn={978-989-758-248-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - A Multicriteria Evaluation of Hybrid Recommender Systems: On the Usefulness of Input Data Characteristics
SN - 978-989-758-248-6
AU - Silva Fortes R.
AU - R. R. de Freitas A.
AU - André Gonçalves M.
PY - 2017
SP - 623
EP - 633
DO - 10.5220/0006315406230633