Although modern recommendation systems can exploit the structure in users' item feedback, most are powerless in the face of new users who provide no structure for them to exploit. In this paper we introduce ImplicitCE, an algorithm for recommending items to new users during their sign-up flow. ImplicitCE works by transforming users' implicit feedback towards auxiliary domain items into an embedding in the target domain item embedding space. ImplicitCE learns these embedding spaces and transformation function in an end-to-end fashion and can co-embed users and items with any differentiable similarity function. To train ImplicitCE we explore methods for maximizing the correlations between model predictions and users' affinities and introduce Sample Correlation Update, a novel and extremely simple training strategy. Finally, we show that ImplicitCE trained with Sample Correlation Update outperforms a variety of state of the art algorithms and loss functions on both a large scale Twitter dataset and the DBLP dataset.

本研究提出了一种名为ImplicitCE的算法，利用新用户的辅助领域项目的隐含反馈来向其推荐物品，并通过最大化模型预测与用户好感度之间的相关性来最大化训练效果。结果表明，使用Sample Correlation Update的ImplicitCE算法在Twitter数据集和DBLP数据集上均优于其他现有算法和损失函数。

一种用于跨领域协同嵌入的相关性最大化方法