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• W4313889305 abstract "Given the increasing growth of the Web and consequently the growth of e-commerce, the application of recommendation systems becomes more and more extensive. A good recommendation algorithm can provide a better user experience. In the collaborative filtering algorithm recommendation system, many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings, this paper proposes an improved constrained Bayesian probability matrix factorization algorithm. The algorithm introduces a potential similarity constraint matrix for specific sparsely scored users to affect the user’s feature vector, and uses the Logistic function to express the nonlinear relationship of the potential factors, combined with the Markov chain Monte Carlo method for training. Finally, the data set is used for testing and comparative evaluation. This experiment proves that the algorithmic model can be efficiently trained using Markov chain Monte Carlo methods by applying them to the MovieLens and Netflix dataset. The experimental results show that the algorithm has better predictive performance and is suitable for solving the problem of sparse rating matrix of specific users." @default.
• W4313889305 created "2023-01-10" @default.
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• W4313889305 date "2023-01-09" @default.
• W4313889305 modified "2023-10-18" @default.
• W4313889305 title "An improved constrained Bayesian probabilistic matrix factorization algorithm" @default.
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• W4313889305 doi "https://doi.org/10.1007/s00500-022-07799-x" @default.
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