Despite the increasing interest towards session-based and streaming recommender systems, there is still a lack of publicly available evaluation frameworks supporting both these paradigms. To address the gap, we propose FlowRec — an extension of the streaming framework Scikit-Multiflow, which opens plentiful possibilities for prototyping recommender systems operating on sessionized data streams, thanks to the underlying collection of incremental learners and support for real-time performance tracking. We describe the extended functionalities of the adapted prequential evaluation protocol, and develop a competitive recommendation algorithm on top of Scikit-Multiflow’s implementation of a Hoeffding Tree. We compare our algorithm to other known baselines for the next-item prediction task across three different domains.
CITATION STYLE
Paraschakis, D., & Nilsson, B. J. (2020). FlowRec: Prototyping Session-Based Recommender Systems in Streaming Mode. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12084 LNAI, pp. 65–77). Springer. https://doi.org/10.1007/978-3-030-47426-3_6
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