2024/10/02 by Andreea Iana, Iana, Andreea, Goran Glavaš +3 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #H.3.3 #I.2.7 #Information Retrieval (cs.IR) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2410.01470
openalex publication_date 2024/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.