Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through historical user-item interactions, which may potentially violate user privacy. Additionally, these approaches often overlook the significance of the temporal fluctuation in item popularity that can sway users' decision-making. To bridge this gap, we propose Popularity-Aware Recommender (PARE), which makes non-personalized recommendations by predicting the items that will attain the highest popularity. PARE consists of four modules, each focusing on a different aspect: popularity history, temporal impact, periodic impact, and side information. Finally, an attention layer is leveraged to fuse the outputs of four modules. To our knowledge, this is the first work to explicitly model item popularity in recommendation systems. Extensive experiments show that PARE performs on par or even better than sophisticated state-of-the-art recommendation methods. Since PARE prioritizes item popularity over personalized user preferences, it can enhance existing recommendation methods as a complementary component. Our experiments demonstrate that integrating PARE with existing recommendation methods significantly surpasses the performance of standalone models, highlighting PARE's potential as a complement to existing recommendation methods. Furthermore, the simplicity of PARE makes it immensely practical for industrial applications and a valuable baseline for future research.

翻译：推荐系统近年来持续受到研究关注。现有推荐方法大多侧重于通过历史用户-物品交互来捕捉用户的个性化偏好，这可能潜在地侵犯用户隐私。此外，这些方法往往忽视了物品流行度的时序波动对用户决策的重要影响。为弥补这一不足，我们提出了一种流行度感知推荐器（PARE），通过预测未来流行度最高的物品来实现非个性化推荐。PARE由四个模块组成，分别聚焦于不同维度：流行度历史、时间影响、周期影响及辅助信息。最终利用注意力层融合四个模块的输出。据我们所知，这是首次在推荐系统中显式建模物品流行度的工作。大量实验表明，PARE的性能可媲美甚至超越当前最先进的推荐方法。由于PARE优先考虑物品流行度而非个性化偏好，它可作为补充组件增强现有推荐方法。实验证明，将PARE与现有推荐方法集成后，性能显著超越各自独立运行的模型，凸显了其作为现有推荐方法补充的潜力。此外，PARE的简洁性使其在工业应用中极具实用性，并为未来研究提供了有价值的基准。