Abstract
Collaborative Filtering (CF) is one of the most successful techniques in recommender systems. Most CF scenarios depict positive-only implicit feedback, which means that negative feedback is unavailable. Therefore, One-Class Collaborative Filtering (OCCF)techniques have been tailored to tackling these scenarios. Nonetheless, several OCCF models still require negative observations during training, and thus, a popular approach is to consider randomly selected unknown relationships as negative. This work brings forward a novel and non-random approach for selecting negative items called Unknown Item Rejection Framework (UKIRF). More specifically, we instantiate UKIRF using similarity approaches, i.e., TF-IDF and Cosine, to reject items similar to those a user interacted with. We apply UKIRF to different OCCF models in different datasets and show that it improves the recall rates up to 24% when compared to random sampling.
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Viniski, A.D., Barddal, J.P., de Souza Britto, A. (2021). UKIRF: An Item Rejection Framework for Improving Negative Items Sampling in One-Class Collaborative Filtering. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12713. Springer, Cham. https://doi.org/10.1007/978-3-030-75765-6_44
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