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- W4365420745 abstract "Collaborative filtering (CF) plays a key role in recommender systems, which consists of two basic disciplines: neighborhood methods and latent factor models. Neighborhood methods are most effective at capturing the very localized structure of a given rating matrix, while latent factor models are generally effective at capturing its global structure. However, two disciplines fail to capture these two structures simultaneously. Motivated by the sparse linear methods and the recently developed capsule networks, we propose a new matrix factorization model for collaborative filtering based on sparse linear capsule networks, which attempts to embed the neighborhood information into latent factors and finally get the very localized and global structure of a given rating matrix. Experiments on real-world datasets demonstrate that our model outperforms seven state-of-the-art matrix factorization-based CF methods in terms of rating prediction accuracy." @default.
- W4365420745 created "2023-04-14" @default.
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- W4365420745 date "2023-01-01" @default.
- W4365420745 modified "2023-10-02" @default.
- W4365420745 title "Sparse Linear Capsules for Matrix Factorization-Based Collaborative Filtering" @default.
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- W4365420745 doi "https://doi.org/10.1007/978-3-031-30105-6_40" @default.
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