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- W3070660065 abstract "Variational autoencoders (VAEs) have proven to be successful in the field of recommender systems. The advantage of this non-linear probabilistic generative model is that it can break through the limited modeling capabilities of linear models which dominate collaborative filtering research to a large extend. In this paper, we propose a deep generative recommendation model by enforcing a list-wise ranking strategy to VAE with the aid of multinomial likelihood. This model has ability to simultaneously generate the point-wise implicit feedback data and create the list-wise ranking list for each user. To seamlessly combine ranking loss with VAE loss, the Reciprocal Rank (RR) is adopted here and approximated with a smoothed function. A series of experiments on two real-world datasets (MovieLens-100k and XuetangX) have been conducted. We show that maximizing the ranking loss will cause as many relevant items appearing at the top of the predicted recommendation list as possible. The experimental results demonstrated that the proposed method outperforms several state-of-the-art methods in ranking estimation task." @default.
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- W3070660065 date "2020-01-01" @default.
- W3070660065 modified "2023-09-24" @default.
- W3070660065 title "Deep Generative Recommendation with Maximizing Reciprocal Rank" @default.
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- W3070660065 doi "https://doi.org/10.1007/978-3-030-55393-7_12" @default.
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