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- W2935521657 abstract "In this paper, we present PRIN, a probabilistic collaborative filtering approach for top-N recommendation. Our proposal relies on continuous bag-of-words (CBOW) neural model. This fully connected feedforward network takes as input the item profile and produces as output the conditional probabilities of the users given the item. With that information, our model produces item recommendations through Bayesian inversion. The inversion requires the estimation of item priors. We propose different estimates based on centrality measures on a graph that models user-item interactions. An exhaustive evaluation of this proposal shows that our technique outperforms popular state-of-the-art baselines regarding ranking accuracy while showing good values of diversity and novelty." @default.
- W2935521657 created "2019-04-11" @default.
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- W2935521657 date "2019-01-01" @default.
- W2935521657 modified "2023-09-25" @default.
- W2935521657 title "PRIN: A Probabilistic Recommender with Item Priors and Neural Models" @default.
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- W2935521657 doi "https://doi.org/10.1007/978-3-030-15712-8_9" @default.
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