In recent years, neural networks and other complex models have dominated recommender systems, often setting new benchmarks for state-of-the-art performance. Yet, despite these advancements, award-winning research has demonstrated that traditional matrix factorization methods can remain competitive, offering simplicity and reduced computational overhead. Hybrid models, which combine matrix factorization with newer techniques, are increasingly employed to harness the strengths of multiple approaches. This paper proposes a novel ensemble method that unifies user-item and item-item recommendations through a weighted similarity framework to deliver top-N recommendations. Our approach is distinctive in its use of shared user and item embeddings for both recommendation strategies, simplifying the architecture and enhancing computational efficiency. Extensive experiments across multiple datasets show that our method achieves competitive performance and is robust in varying scenarios that favor either user-item or item-item recommendations. Additionally, by eliminating the need for embedding-specific fine-tuning, our model allows for the seamless reuse of hyperparameters from the base algorithm without sacrificing performance. This results in a method that is both efficient and easy to implement. Our open-source implementation is available at https://github.com/UFSCar-LaSID/weighted-sims-recommender.

翻译：近年来，神经网络及其他复杂模型主导了推荐系统领域，常常树立起新的性能标杆。然而，尽管取得这些进展，获奖研究已表明，传统的矩阵分解方法仍具竞争力，其优势在于简洁性及较低的计算开销。结合矩阵分解与新兴技术的混合模型被日益采用，以融合多种方法的优势。本文提出一种新颖的集成方法，通过加权相似度框架统一基于用户-物品与基于物品-物品的推荐，以生成Top-N推荐。本方法的独特之处在于，它对两种推荐策略共享用户与物品嵌入，从而简化架构并提升计算效率。在多个数据集上的大量实验表明，本方法性能具有竞争力，并在偏好用户-物品或物品-物品推荐的多种场景下均表现稳健。此外，由于无需针对嵌入进行特定微调，本模型可无缝复用基础算法的超参数，且不牺牲性能。这使其成为一种既高效又易于实现的方法。我们的开源实现代码可从 https://github.com/UFSCar-LaSID/weighted-sims-recommender 获取。