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- W2889179871 abstract "Recommendation is a technique which helps and suggests a user, any relevant item from a large information space. Current techniques for this purpose include non-probabilistic methods like content-based filtering and collaborative filtering (CF) and probabilistic methods like Bayesian inference and Case-based reasoning methods. CF algorithms use similarity measures for calculating similarity between users. In this paper, we propose a novel framework which deterministically switches between the CF algorithms based on sparsity to improve accuracy of recommendation." @default.
- W2889179871 created "2018-09-07" @default.
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- W2889179871 date "2018-09-02" @default.
- W2889179871 modified "2023-09-26" @default.
- W2889179871 title "A Novel Deterministic Framework for Non-probabilistic Recommender Systems" @default.
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- W2889179871 doi "https://doi.org/10.1007/978-981-13-1498-8_8" @default.
- W2889179871 hasPublicationYear "2018" @default.
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