Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives

TL;DR

This paper introduces a unified framework for neural news recommendation, demonstrating that simpler models and contrastive training can outperform complex architectures and traditional objectives.

Contribution

It systematically compares different design choices and training objectives, revealing that efficient late fusion and contrastive learning improve performance.

Findings

Late fusion often outperforms complex user encoders.

Contrastive training is a viable alternative to classification.

Simpler models can achieve better results.

Abstract

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.