The advent of personalized news recommendation has given rise to increasingly complex recommender architectures. Most neural news recommenders rely on user click behavior and typically introduce dedicated user encoders that aggregate the content of clicked news into user embeddings (early fusion). These models are predominantly trained with standard point-wise classification objectives. The existing body of work exhibits two main shortcomings: (1) despite general design homogeneity, direct comparisons between models are hindered by varying evaluation datasets and protocols; (2) it leaves alternative model designs and training objectives vastly unexplored. In this work, we present a unified framework for news recommendation, allowing for a systematic and fair comparison of news recommenders across several crucial design dimensions: (i) candidate-awareness in user modeling, (ii) click behavior fusion, and (iii) training objectives. Our findings challenge the status quo in neural news recommendation. We show that replacing sizable user encoders with parameter-efficient dot products between candidate and clicked news embeddings (late fusion) often yields substantial performance gains. Moreover, our results render contrastive training a viable alternative to point-wise classification objectives.

翻译：个性化新闻推荐的兴起催生了日益复杂的推荐架构。大多数神经新闻推荐系统依赖用户点击行为，通常引入专用用户编码器，将点击新闻的内容聚合为用户嵌入（早期融合）。这些模型主要采用标准逐点分类目标进行训练。现有研究存在两个主要缺陷：（1）尽管整体设计同质性较高，但不同评估数据集与协议导致模型间难以直接比较；（2）替代性模型设计与训练目标基本未被探索。本研究提出统一的新闻推荐框架，在多个关键设计维度上实现新闻推荐系统的系统化公平比较：用户建模中的候选感知性、点击行为融合方式及训练目标。我们的发现挑战了神经新闻推荐的现有范式。研究表明，用候选新闻与点击新闻嵌入间的参数高效点积（晚期融合）替代大规模用户编码器，通常能带来显著的性能提升。此外，我们的结果将对比学习训练确立为逐点分类目标的可行替代方案。