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- W4200488753 abstract "Conventional sequential recommender system uses content features extracted by a hand-craft method, and a pre-trained deep learning method. This causes the utility gap between content features and the purpose of sequential recommendations. To reduce the utility gap, we propose a sequential recommender system with end-to-end title feature extraction. Specifically, we develop a convolutional text feature extraction method for title features. Because we can optimize text features for the sequential recommendation in the end-to-end manner, we can reduce the utility gap. Experimental results for real educational contents and teachers’ operating histories on Forestanet show the effectiveness of our proposed method." @default.
- W4200488753 created "2021-12-31" @default.
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- W4200488753 date "2021-10-12" @default.
- W4200488753 modified "2023-10-16" @default.
- W4200488753 title "Sequential Recommender System of Educational Contents with End-to-End Title Feature Extraction for Reducing Utility Gap" @default.
- W4200488753 doi "https://doi.org/10.1109/gcce53005.2021.9621956" @default.
- W4200488753 hasPublicationYear "2021" @default.
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