Abstract
We introduce a new hybrid recommendation method that is based on four data processing steps: 1) preprocessing of content features describing items, 2) preliminary dimensionality reduction applied to user/item vectors expressed in content features space (performed by means of SVD), 3) augmentation of normalized low-dimensional preliminary user/item vectors according to collaborative filtering data and leading to the reconstruction of user/item vectors (based on final item/user vectors and the original input matrix), and 4) the estimation of missing entries in the user-item ratings matrix. In the experiments presented in the paper, we focus on the most challenging case of extreme collaborative data sparsity. We show that a low-dimensional space is suitable for recommendation generation, despite collaborative data sparsity disqualifying the use of methods widely referenced in the relevant literature. In particular, we demonstrate that the proposed low-dimensional feature augmentation method is more effective than the well-known weighted feature combination method.
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Szwabe, A., Janasiewicz, T., Ciesielczyk, M. (2011). Hybrid Recommendation Based on Low-Dimensional Augmentation of Combined Feature Profiles. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23938-0_3
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DOI: https://doi.org/10.1007/978-3-642-23938-0_3
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