Sequential Recommender Systems (SRSs) have emerged as a highly efficient approach to recommendation systems. By leveraging sequential data, SRSs can identify temporal patterns in user behaviour, significantly improving recommendation accuracy and relevance.Ensuring the reproducibility of these models is paramount for advancing research and facilitating comparisons between them. Existing works exhibit shortcomings in reproducibility and replicability of results, leading to inconsistent statements across papers. Our work fills these gaps by standardising data pre-processing and model implementations, providing a comprehensive code resource, including a framework for developing SRSs and establishing a foundation for consistent and reproducible experimentation. We conduct extensive experiments on several benchmark datasets, comparing various SRSs implemented in our resource. We challenge prevailing performance benchmarks, offering new insights into the SR domain. For instance, SASRec does not consistently outperform GRU4Rec. On the contrary, when the number of model parameters becomes substantial, SASRec starts to clearly dominate all the other SRSs. This discrepancy underscores the significant impact that experimental configuration has on the outcomes and the importance of setting it up to ensure precise and comprehensive results. Failure to do so can lead to significantly flawed conclusions, highlighting the need for rigorous experimental design and analysis in SRS research. Our code is available at https://github.com/antoniopurificato/recsys_repro_conf.

翻译：顺序推荐系统已成为推荐系统领域一种高效的方法。通过利用顺序数据，顺序推荐系统能够识别用户行为中的时序模式，显著提升推荐的准确性和相关性。确保这些模型的可复现性对于推动研究进展和促进模型间比较至关重要。现有研究在结果的可复现性和可复制性方面存在不足，导致不同论文中的结论存在不一致。本研究通过标准化数据预处理和模型实现填补了这些空白，提供了完整的代码资源，包括开发顺序推荐系统的框架，并为一致且可复现的实验奠定了基础。我们在多个基准数据集上进行了大量实验，比较了资源中实现的各种顺序推荐系统。我们对当前主流的性能基准提出了质疑，为顺序推荐领域提供了新的见解。例如，SASRec 并非始终优于 GRU4Rec。相反，当模型参数量变得足够大时，SASRec 开始明显优于所有其他顺序推荐系统。这种差异凸显了实验配置对结果的重大影响，以及正确设置配置以确保精确全面结果的重要性。若未能做到这一点，可能导致严重错误的结论，这强调了在顺序推荐系统研究中需要严谨的实验设计与分析。我们的代码发布于 https://github.com/antoniopurificato/recsys_repro_conf。