Matrix factorization is one of the most efficient approaches in recommender systems. However, such algorithms, which rely on the interactions between users and items, perform poorly for "cold-users" (users with little history of such interactions) and at capturing the relationships between closely related items. To address these problems, we propose a neural personalized embedding (NPE) model, which improves the recommendation performance for cold-users and can learn effective representations of items. It models a user's click to an item in two terms: the personal preference of the user for the item, and the relationships between this item and other items clicked by the user. We show that NPE outperforms competing methods for top-N recommendations, specially for cold-user recommendations. We also performed a qualitative analysis that shows the effectiveness of the representations learned by the model.
翻译:矩阵化是建议者系统中最有效的方法之一,然而,这种依赖用户和项目之间相互作用的算法对“冷却用户”(这种相互作用的用户很少)和捕捉密切相关项目之间的关系效果不佳。为了解决这些问题,我们提议了一个神经化个性化嵌入模型,该模型可以改进冷却用户的建议性能,并能够了解项目的有效表述。它用两个术语模拟用户点击一个项目:用户个人对项目的偏好,以及该项目与用户点击的其他项目之间的关系。我们表明,NPE优于顶级建议的竞争方法,特别是冷却用户建议。我们还进行了质量分析,以显示模型所了解的表述的有效性。