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- W2313952682 abstract "The existing collaborative recommendation algorithms suffer from lower recommendation precision due to the problem of data sparsity. To solve this problem, we propose a novel collaborative recommendation algorithm which integrates the probabilistic matrix factorization and neighbor models. We first propose a method to calculate the similarity between users or items based on the probabilistic matrix factorization model and construct a natural exponential function to compute the weighted similarity. Then we devise a collaborative recommendation algorithm to make recommendations for the target user, which dynamically adjusts the recommendation results for user- and item-based models by the balance adjustment factor. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms the existing algorithms in terms of prediction accuracy." @default.
- W2313952682 created "2016-06-24" @default.
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- W2313952682 date "2015-03-20" @default.
- W2313952682 modified "2023-09-24" @default.
- W2313952682 title "A Novel Collaborative Recommendation Algorithm Integrating Probabilistic Matrix Factorization and Neighbor Model" @default.
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- W2313952682 doi "https://doi.org/10.12733/jics20105604" @default.
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