  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.
