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- W2980521590 abstract "Exploiting the selections of social friends and foes can efficiently face the data scarcity of user preferences and the cold-start problem. In this paper, we present a Social Deep Pairwise Learning model, namely SDPL. According to the Bayesian Pairwise Ranking criterion, we design a loss function with multiple ranking criteria based on the selections of users, and those in their friends and foes to improve the accuracy in the top-k recommendation task. We capture the nonlinearity in user preferences and the social information of trust and distrust relationships by designing a deep learning architecture. In each backpropagation step, we perform social negative sampling to meet the multiple ranking criteria of our loss function. Our experiments on a benchmark dataset from Epinions, among the largest publicly available that has been reported in the relevant literature, demonstrate the effectiveness of the proposed approach, outperforming other state-of-the art methods. In addition, we show that our deep learning strategy plays an important role in capturing the nonlinear associations between user preferences and the social information of trust and distrust relationships, and demonstrate that our social negative sampling strategy is a key factor in SDPL." @default.
- W2980521590 created "2019-10-25" @default.
- W2980521590 creator A5034151597 @default.
- W2980521590 date "2019-10-14" @default.
- W2980521590 modified "2023-09-26" @default.
- W2980521590 title "Bayesian Deep Learning with Trust and Distrust in Recommendation Systems" @default.
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- W2980521590 doi "https://doi.org/10.1145/3350546.3352496" @default.
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