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- W4313013309 abstract "This paper proposes a text-based recommendation system based on Probabilistic Matrix Factorization. ConvMF is one of the successful text-based PMF models, which uses CNN to capture features of items from the item description text and integrates them into PMF. However, ConvMF does not take user characteristics into account, and the effectiveness of item description text and user description text has not been evaluated. Therefore, this paper proposes a method that uses a CNN for ConvMF, extracts features from user description text created from user reviews, and integrates them into PMF as user features. The proposed method improves the recommendation accuracy by 2.8% to 4.4% over existing methods on a dataset of five genres of books. We also evaluated the effectiveness of each book genre and examined the effectiveness of the pre-training model. We also evaluated the robustness of the proposed method by parameter analysis. The proposed method was shown to be less affected by parameters than existing methods and to be more robust." @default.
- W4313013309 created "2023-01-05" @default.
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- W4313013309 date "2022-01-01" @default.
- W4313013309 modified "2023-09-26" @default.
- W4313013309 title "Book Recommender System Using CNN Capturing Feature of Synopses and Reviews" @default.
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- W4313013309 doi "https://doi.org/10.1007/978-3-031-21707-4_37" @default.
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