论文标题
稀疏正规化以进行冷启动建议
Sparsity Regularization For Cold-Start Recommendation
论文作者
论文摘要
最近，生成的对抗网络（GAN）已应用于冷启动建议的问题，但是这些模型的训练性能受到温暖用户购买行为的极端稀疏性的阻碍。在本文中，我们通过组合用户人口统计和用户偏好来介绍一个新颖的用户媒介表示，使该模型成为了使用协作过滤和基于内容的建议的混合系统。我们的系统模型使用加权用户产品首选项（明确反馈）而不是二进制用户产品交互（隐性反馈）的用户购买行为。使用此功能，我们开发了一种新颖的稀疏对抗模型Srlgan，以降低启动建议，以利用稀疏的用户购买行为，从而确保训练稳定性并避免过度适合温暖的用户。我们在两个流行的数据集上评估了Srlgan，并展示了最先进的结果。
Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm user purchase behavior. In this paper we introduce a novel representation for user-vectors by combining user demographics and user preferences, making the model a hybrid system which uses Collaborative Filtering and Content Based Recommendation. Our system models user purchase behavior using weighted user-product preferences (explicit feedback) rather than binary user-product interactions (implicit feedback). Using this we develop a novel sparse adversarial model, SRLGAN, for Cold-Start Recommendation leveraging the sparse user-purchase behavior which ensures training stability and avoids over-fitting on warm users. We evaluate the SRLGAN on two popular datasets and demonstrate state-of-the-art results.
