Effect of Item Representation and Item Comparison Models on Metrics for Surprise in Recommender Systems

Andre Paulino de Lima, Sarajane Marques Peres

2019

Abstract

Surprise is a property of recommender systems that has been receiving increasing attention owing to its links to serendipity. Most of the metrics for surprise poorly agree with definitions employed in research areas that conceptualise surprise as a human factor, and because of this, their use in the task of evaluating recommendations may not produce the desired effect. We argue that metrics with the characteristics that are presumed by models of surprise from the Cognitive Science may be more successful in that task. Moreover, we show that a metric for surprise is sensitive to the choices of how items are represented and compared by the recommender. In this paper, we review metrics for surprise in recommender systems, and analyse to which extent they align to two competing cognitive models of surprise. For that metric with the highest agreement, we conducted an off-line experiment to estimate the effect exerted on surprise by choices of item representation and comparison. We explore 56 recommenders that vary in recommendation algorithms, and item representation and comparison. The results show a large interaction between item representation and item comparison, which suggests that new distance functions can be explored to promote serendipity in recommendations.

Paper Citation

in Harvard Style

Paulino de Lima A. and Peres S. (2019). Effect of Item Representation and Item Comparison Models on Metrics for Surprise in Recommender Systems.In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-372-8, pages 513-524. DOI: 10.5220/0007677005130524

in Bibtex Style

@conference{iceis19,
author={Andre Paulino de Lima and Sarajane Peres},
title={Effect of Item Representation and Item Comparison Models on Metrics for Surprise in Recommender Systems},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2019},
pages={513-524},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007677005130524},
isbn={978-989-758-372-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Effect of Item Representation and Item Comparison Models on Metrics for Surprise in Recommender Systems
SN - 978-989-758-372-8
AU - Paulino de Lima A.
AU - Peres S.
PY - 2019
SP - 513
EP - 524
DO - 10.5220/0007677005130524