In this paper, we present sequeval, a software tool capable of performing the offline evaluation of a recommender system designed to suggest a sequence of items. A sequence-based recommender is trained considering the sequences already available in the system and its purpose is to generate a personalized sequence starting from an initial seed. This tool automatically evaluates the sequence-based recommender considering a comprehensive set of eight different metrics adapted to the sequential scenario. sequeval has been developed following the best practices of software extensibility. For this reason, it is possible to easily integrate and evaluate novel recommendation techniques. sequeval is publicly available as an open source tool and it aims to become a focal point for the community to assess sequence-based recommender systems.
@article{arxiv.1810.04956,
title = {Sequeval: A Framework to Assess and Benchmark Sequence-based Recommender Systems},
author = {Diego Monti and Enrico Palumbo and Giuseppe Rizzo and Maurizio Morisio},
journal= {arXiv preprint arXiv:1810.04956},
year = {2018}
}
Comments
REVEAL 2018 Workshop on Offline Evaluation for Recommender Systems