Graduate School and Research Center in Digital Sciences

Sequeval: A framework to assess and benchmark sequence-based recommender systems

Monti, Diego; Palumbo, Enrico; Rizzo, Giuseppe; Morisio, Maurizio

Submitted on ArXiV, 11 October 2018

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.

Arxiv Bibtex

Title:Sequeval: A framework to assess and benchmark sequence-based recommender systems
Type:Conference
Language:English
City:
Date:
Department:Data Science
Eurecom ref:5713
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Submitted on ArXiV, 11 October 2018 and is available at :
Bibtex: @inproceedings{EURECOM+5713, year = {2018}, title = {{S}equeval: {A} framework to assess and benchmark sequence-based recommender systems}, author = {{M}onti, {D}iego and {P}alumbo, {E}nrico and {R}izzo, {G}iuseppe and {M}orisio, {M}aurizio}, booktitle = {{S}ubmitted on {A}r{X}i{V}, 11 {O}ctober 2018}, address = {}, month = {10}, url = {http://www.eurecom.fr/publication/5713} }
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