Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in the research literature. These algorithms base their recommendations solely on the observed interactions with the user in an ongoing session and do not require the existence of long-term preference profiles. Most recently, a number of deep learning based ("neural") approaches to session-based recommendations were proposed. However, previous research indicates that today's complex neural recommendation methods are not always better than comparably simple algorithms in terms of prediction accuracy. With this work, our goal is to shed light on the state-of-the-art in the area of session-based recommendation and on the progress that is made with neural approaches. For this purpose, we compare twelve algorithmic approaches, among them six recent neural methods, under identical conditions on various datasets. We find that the progress in terms of prediction accuracy that is achieved with neural methods is still limited. In most cases, our experiments show that simple heuristic methods based on nearest-neighbors schemes are preferable over conceptually and computationally more complex methods. Observations from a user study furthermore indicate that recommendations based on heuristic methods were also well accepted by the study participants. To support future progress and reproducibility in this area, we publicly share the session-rec evaluation framework that was used in our research.

中文翻译：

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基于会话的推荐算法的实证分析

推荐系统是通过在信息过载的情况下将在线用户指向潜在感兴趣的项目来支持在线用户的工具。近年来，基于会话的推荐算法类在研究文献中受到更多关注。这些算法仅基于在正在进行的会话中观察到的与用户的交互来推荐它们，并且不需要存在长期的偏好配置文件。最近，提出了许多基于深度学习（“神经”）的基于会话的推荐方法。然而，之前的研究表明，就预测精度而言，当今复杂的神经推荐方法并不总是优于相对简单的算法。通过这项工作，我们的目标是阐明基于会话的推荐领域的最新技术以及神经方法取得的进展。为此，我们在不同数据集的相同条件下比较了十二种算法方法，其中包括六种最近的神经方法。我们发现，使用神经方法实现的预测准确性方面的进展仍然有限。在大多数情况下，我们的实验表明，基于最近邻方案的简单启发式方法优于概念上和计算上更复杂的方法。来自用户研究的观察进一步表明，基于启发式方法的推荐也被研究参与者很好地接受。为了支持该领域的未来进展和可重复性，