Peeling Back the Layers: An In-Depth Evaluation of Encoder Architectures in Neural News Recommenders

TL;DR

This paper conducts a detailed analysis of encoder architectures in neural news recommenders, revealing that simpler models can often match or outperform complex ones, thereby guiding more efficient design choices.

Contribution

It provides a systematic evaluation of encoder architectures focusing on representational similarity, list overlap, and recommendation performance, offering nuanced insights beyond overall accuracy.

Findings

Complex encoding techniques are often empirically unjustified.

Simpler architectures can achieve comparable or better performance.

Insights help avoid unnecessary complexity in news recommender systems.

Abstract

Encoder architectures play a pivotal role in neural news recommenders by embedding the semantic and contextual information of news and users. Thus, research has heavily focused on enhancing the representational capabilities of news and user encoders to improve recommender performance. Despite the significant impact of encoder architectures on the quality of news and user representations, existing analyses of encoder designs focus only on the overall downstream recommendation performance. This offers a one-sided assessment of the encoders' similarity, ignoring more nuanced differences in their behavior, and potentially resulting in sub-optimal model selection. In this work, we perform a comprehensive analysis of encoder architectures in neural news recommender systems. We systematically evaluate the most prominent news and user encoder architectures, focusing on their (i) representational similarity, measured with the Central Kernel Alignment, (ii) overlap of generated recommendation lists, quantified with the Jaccard similarity, and (iii) the overall recommendation performance. Our analysis reveals that the complexity of certain encoding techniques is often empirically unjustified, highlighting the potential for simpler, more efficient architectures. By isolating the effects of individual components, we provide valuable insights for researchers and practitioners to make better informed decisions about encoder selection and avoid unnecessary complexity in the design of news recommenders.