Recent neural news recommenders (NNR) extend content-based recommendation by (1) aligning additional aspects such as topic or sentiment between the candidate news and user history or (2) diversifying recommendations w.r.t. these aspects. This customization is achieved by ``hardcoding'' additional constraints into NNR's architecture and/or training objectives: any change in the desired recommendation behavior thus requires the model to be retrained with a modified objective, impeding wide adoption of multi-aspect news recommenders. In this work, we introduce MANNeR, a modular framework for flexible multi-aspect (neural) news recommendation that supports ad-hoc customization over individual aspects at inference time. With metric-based learning at its core, MANNeR obtains aspect-specialized news encoders and then flexibly combines aspect-specific similarity scores for final ranking. Evaluation on two standard news recommendation benchmarks (one in English, one in Norwegian) shows that MANNeR consistently outperforms state-of-the-art NNRs on both standard content-based recommendation and single- and multi-aspect customization. Moreover, with MANNeR we can trivially scale the importance and find the optimal trade-off between content-based recommendation performance and aspect-based diversity of recommendations. Finally, we show that both MANNeR's content-based recommendation and aspect customization are robust to domain- and language transfer.

翻译：近期神经新闻推荐器（NNR）通过（1）将候选新闻与用户历史之间的主题或情感等额外方面进行对齐，或（2）针对这些方面进行推荐多样化，扩展了基于内容的推荐。这种定制通过将额外约束“硬编码”到NNR的架构和/或训练目标中实现：对期望推荐行为的任何改变都需要用修改后的目标重新训练模型，这阻碍了多方面新闻推荐器的广泛采用。在本文中，我们提出了MANNeR，一个用于灵活多方面（神经）新闻推荐的模块化框架，支持在推理时对单个方面进行临时定制。以基于度量的学习为核心，MANNeR获得方面专精的新闻编码器，然后灵活组合方面特定的相似度得分进行最终排序。在两个标准新闻推荐基准（一个英文，一个挪威文）上的评估表明，MANNeR在标准基于内容推荐以及单方面和多方面定制上均持续优于最先进的NNR。此外，使用MANNeR，我们可以轻松调整重要性权重，并找到基于内容推荐性能与基于方面的推荐多样性之间的最优权衡。最后，我们证明MANNeR的基于内容推荐和方面定制均对领域和语言迁移具有鲁棒性。