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Bunga, R., Batista, F. & Ribeiro, R. (2021). From implicit preferences to ratings: Video games recommendation based on collaborative filtering. In Cucchiara, R., Fred, A., & Filipe, J. (Ed.), Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. (pp. 209-216).: SCITEPRESS – Science and Technology Publications, Lda.

R. Bunga et al.,  "From implicit preferences to ratings: Video games recommendation based on collaborative filtering", in Proc. of the 13th Int. Joint Conf. on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Cucchiara, R., Fred, A., & Filipe, J., Ed., SCITEPRESS – Science and Technology Publications, Lda, 2021, vol. 1, pp. 209-216

@inproceedings{bunga2021_1730765922585,
	author = "Bunga, R. and Batista, F. and Ribeiro, R.",
	title = "From implicit preferences to ratings: Video games recommendation based on collaborative filtering",
	booktitle = "Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
	year = "2021",
	editor = "Cucchiara, R., Fred, A., & Filipe, J.",
	volume = "1",
	number = "",
	series = "",
	doi = "10.5220/0010655900003064",
	pages = "209-216",
	publisher = "SCITEPRESS – Science and Technology Publications, Lda",
	address = "",
	organization = "INSTICC - Institute for Systems and Technologies of Information, Control and Communication",
	url = "https://www.scitepress.org/ProceedingsDetails.aspx?ID=cBXMJ72+CEw=&t=1"
}

TY  - CPAPER
TI  - From implicit preferences to ratings: Video games recommendation based on collaborative filtering
T2  - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
VL  - 1
AU  - Bunga, R.
AU  - Batista, F.
AU  - Ribeiro, R.
PY  - 2021
SP  - 209-216
SN  - 2184-3228
DO  - 10.5220/0010655900003064
UR  - https://www.scitepress.org/ProceedingsDetails.aspx?ID=cBXMJ72+CEw=&t=1
AB  - This work studies and compares the performance of collaborative filtering algorithms, with the intent of proposing a videogame-oriented recommendation system. This system uses information from the video game platform Steam, which contains information about the game usage, corresponding to the implicit feedback that was later transformed into explicit feedback. These algorithms were implemented using the Surprise library, that allows to create and evaluate recommender systems that deal with explicit data. The algorithms are evaluated and compared with each other using metrics such as RSME, MAE, Precision@k, Recall@k and F1@k. We have concluded that computationally low demanding approaches can still obtain suitable results.
ER  -