PP4RNR: Popularity- and Position-Aware Contrastive Learning for Retrieval-Driven News Recommendation

Existing news recommendation systems often overlook the diversity of recommended content and exhibit popularity bias, resulting in suboptimal performance. To address this issue, this paper introduces a novel news recommendation approach, Popularity- and Position-Aware Contrastive Learning for Retrieval-Driven News Recommendation (PP4RNR). It consists of two modules: Entity-Level Retrieval Augmentation (ERA) and Popularity- and Position-Aware Contrastive Learning (PPCL). The ERA module utilizes both entities and titles to retrieve relevant news. Subsequently, retrieval-augmented news is fused with candidate news using our innovative cascaded attention network, leading to richer and more diverse news semantics. The PPCL module introduces perturbations in the news representation using a Gaussian perturbation vector based on the popularity and position information and then employs contrastive learning to regularize the representation space. Hence, this approach not only deepens the understanding of content diversity but also implicitly mitigates the popularity bias prevalent in current models. Rigorous testing on benchmark datasets demonstrates that our method significantly outperforms a range of state-of-the-art techniques.