Abstract
Collaborative filtering is a widely used recommendation technique, which often relies on rating information shared by users, i.e., crowdsourced data. These filters rely on predictive algorithms, such as, memory or model based predictors, to build direct or latent user and item profiles from crowdsourced data. To predict unknown ratings, memory-based approaches rely on the similarity between users or items, whereas model-based mechanisms explore user and item latent profiles. However, many of these filters are opaque by design, leaving users with unexplained recommendations. To overcome this drawback, this paper introduces Explug, a local model-agnostic plug-in that works alongside stream-based collaborative filters to reorder and explain recommendations. The explanations are based on incremental user Trust & Reputation profiling and co-rater relationships. Experiments performed with crowdsourced data from TripAdvisor show that Explug explains and improves the quality of stream-based collaborative filter recommendations.
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Notes
- 1.
Available at https://scikit-multiflow.github.io, January 2022.
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Acknowledgements
This work was partially supported by: (i) Xunta de Galicia grant ED481B-2021-118, Spain; and (ii) Portuguese National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UIDB/50014/2020.
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Leal, F., García-Méndez, S., Malheiro, B., Burguillo, J.C. (2022). Explanation Plug-In for Stream-Based Collaborative Filtering. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-04826-5_5
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