Abstract
Most Recommender Systems rely exclusively on ratings and are known as Memory-based Collaborative Filtering systems. This is currently dominant approach outside of academia due to the low implementation effort and service maintenance, when compared with more complex Model-based approaches, Traditional Memory-based systems have as their main goal to predict ratings, using similarity metrics to determine similarities between the users’ (or items) rating patterns. In this work, we propose a user-based Collaborative Filtering approach based on tags that does not rely on rating prediction, instead leveraging on Fuzzy Fingerprints to create a novel similarity metric. Fuzzy Fingerprints provide a concise and compact representation of users allowing the reduction of the dimensionality usually associated with user-based collaborative filtering. The proposed recommendation strategy combined with the Fuzzy Fingerprint similarity metric is able to outperform our baselines, in the Movielens-1M dataset.
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Acknowledgments
This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2013, by project GoLocal (ref. CMUPERI/TIC/0046/2014) and co-financed by the University of Lisbon and INESC-ID.
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Carvalho, A., Calado, P., Carvalho, J.P. (2018). Tag-Based User Fuzzy Fingerprints for Recommender Systems. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_62
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