一种跨域协同嵌入的相关最大化方法
A Correlation Maximization Approach for Cross Domain Co-Embeddings
摘要：
虽然现代推荐系统可以利用用户的项目反馈中的结构，但大多数推荐系统在面对新用户时无能为力，因为新用户没有提供可供利用的结构。在本文中，我们介绍了隐式CE，推荐项目的新用户在他们的注册流程的算法。ImplicitCE的工作原理是将用户对辅助域项的隐式反馈转换为目标域项嵌入空间中的嵌入。ImplicitCE以端到端的方式学习这些嵌入空间和转换函数，并可以使用任何可微相似性函数共同嵌入用户和项目。为了训练ImplicitCE，我们探索了最大化模型预测和用户亲和力之间的相关性的方法，并引入了样本相关性更新，这是一种新颖且非常简单的训练策略。最后，我们证明了使用样本相关性更新训练的ImplicitCE在大规模Twitter数据集和DBLP数据集上的性能优于各种最先进的算法和损失函数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
