An Off-line Evaluation of Users’ Ranking Metrics in Group Recommendation

Silvia Rossi, Francesco Cervone, Francesco Barile

2017

Abstract

One of the major issue in designing group recommendation techniques relates to the difficulty of the evaluation process. Up-today, no freely available dataset exists that contains information about groups, like, for example, the group’s choices or social aspects that may characterize the group’s members. The objective of the paper is to analyze the possibility to make an evaluation of ranking-based groups recommendation techniques by using offline testing. Typically, the evaluation of group recommendations is computed, as in the classical single user case, by comparing the predicted group’s ratings with respect to the single users’ ratings. Since the information contained in the datasets are mainly such user’s ratings, here, ratings are used to define different ranking metrics. Results suggest that such an attempt is hardly feasible. Performance seems not to be affected by the choice of ranking technique, except for some particular cases. This could be due to the averaging effect of the evaluation with respect to the single users’ ratings, so a deeper analysis or specific dataset are necessary.

Paper Citation

in Harvard Style

Rossi S., Cervone F. and Barile F. (2017). An Off-line Evaluation of Users’ Ranking Metrics in Group Recommendation . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-219-6, pages 252-259. DOI: 10.5220/0006200702520259

in Bibtex Style

@conference{icaart17,
author={Silvia Rossi and Francesco Cervone and Francesco Barile},
title={An Off-line Evaluation of Users’ Ranking Metrics in Group Recommendation},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2017},
pages={252-259},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006200702520259},
isbn={978-989-758-219-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - An Off-line Evaluation of Users’ Ranking Metrics in Group Recommendation
SN - 978-989-758-219-6
AU - Rossi S.
AU - Cervone F.
AU - Barile F.
PY - 2017
SP - 252
EP - 259
DO - 10.5220/0006200702520259