论文标题
了解用户和新闻表示在基于内容的神经新闻建议中的关系
Understanding the Relation of User and News Representations in Content-Based Neural News Recommendation
论文作者
论文摘要
已经提出了许多基于神经内容的新闻建议的模型。但是，对此类系统的三个主要组成部分（新闻编码器，用户编码和评分功能）和所涉及的权衡的相对重要性的了解有限。在本文中，我们评估了以下假设：匹配用户和候选新闻表示的最广泛使用的方法不够表达。我们允许我们的系统通过评估更具表现力的评分功能来建模两者之间的更复杂的关系。在广泛的基线和建立的系统中，这会导致AUC中约6分的一致改进。我们的结果还表明，新闻编码器的复杂性与评分功能之间的权衡：一个相当简单的基线模型在思维数据集中得分远高于68％的AUC，并且在发布最新时间的2点以内，同时需要计算成本的一部分。
A number of models for neural content-based news recommendation have been proposed. However, there is limited understanding of the relative importances of the three main components of such systems (news encoder, user encoder, and scoring function) and the trade-offs involved. In this paper, we assess the hypothesis that the most widely used means of matching user and candidate news representations is not expressive enough. We allow our system to model more complex relations between the two by assessing more expressive scoring functions. Across a wide range of baseline and established systems this results in consistent improvements of around 6 points in AUC. Our results also indicate a trade-off between the complexity of news encoder and scoring function: A fairly simple baseline model scores well above 68% AUC on the MIND dataset and comes within 2 points of the published state-of-the-art, while requiring a fraction of the computational costs.
