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• W3128854470 abstract "Recommender systems have played a vital role in online platforms due to the ability of incorporating users' personal tastes. Beyond accuracy, diversity has been recognized as a key factor in recommendation to broaden user's horizons as well as to promote enterprises' sales. However, the trading-off between accuracy and diversity remains to be a big challenge, and the data and user biases have not been explored yet. In this paper, we develop an adaptive learning framework for accurate and diversified recommendation. We generalize recent proposed bi-lateral branch network in the computer vision community from image classification to item recommendation. Specifically, we encode domain level diversity by adaptively balancing accurate recommendation in the conventional branch and diversified recommendation in the adaptive branch of a bilateral branch network. We also capture user level diversity using a two-way adaptive metric learning backbone network in each branch. We conduct extensive experiments on three real-world datasets. Results demonstrate that our proposed approach consistently outperforms the state-of-the-art baselines." @default.
• W3128854470 created "2021-02-15" @default.
• W3128854470 creator A5040759280 @default.
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• W3128854470 date "2021-01-04" @default.
• W3128854470 modified "2023-09-27" @default.
• W3128854470 title "Recommending Accurate and Diverse Items Using Bilateral Branch Network" @default.
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• W3128854470 doi "https://doi.org/10.48550/arxiv.2101.00781" @default.
• W3128854470 hasPublicationYear "2021" @default.
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