Improving Re-rankCCP with Rules Quality Measures
Piotr Jezusek, Aleksandra Karpus
Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 67–71 (2022)
Abstract. Recommender Systems are software tools and techniques which aim at suggesting new items that may possibly be of interest to a user. Context-Aware Recommender Systems exploit contextual information to provide more adequate recommendations. In this paper we described a modification of an existing contextual post-filtering algorithm which uses rules-like user representation called Contextual Conditional Preferences. We extended the algorithm by taking into account rules quality measures while recommending items to a user. We proved that this modification increases the quality of recommendations, measured with precision, recall and nDCG, and has no impact on the execution time of the original algorithm.
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