Maher Mohamed, Ngoy Perseverance Munga, Rebriks Aleksandrs, Ozcinar Cagri, Cuevas Josue, Sanagavarapu Rajasekhar, Anbarjafari Gholamreza
iCV Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia.
Machine Learning Group, Big Data Department, Rakuten Inc., Tokyo 158-0094, Japan.
Entropy (Basel). 2022 Oct 31;24(11):1575. doi: 10.3390/e24111575.
Boosting the sales of e-commerce services is guaranteed once users find more items matching their interests in a short amount of time. Consequently, recommendation systems have become a crucial part of any successful e-commerce service. Although various recommendation techniques could be used in e-commerce, a considerable amount of attention has been drawn to session-based recommendation systems in recent years. This growing interest is due to security concerns over collecting personalized user behavior data, especially due to recent general data protection regulations. In this work, we present a comprehensive evaluation of the state-of-the-art deep learning approaches used in the session-based recommendation. In session-based recommendation, a recommendation system counts on the sequence of events made by a user within the same session to predict and endorse other items that are more likely to correlate with their preferences. Our extensive experiments investigate baseline techniques (e.g., nearest neighbors and pattern mining algorithms) and deep learning approaches (e.g., recurrent neural networks, graph neural networks, and attention-based networks). Our evaluations show that advanced neural-based models and session-based nearest neighbor algorithms outperform the baseline techniques in most scenarios. However, we found that these models suffer more in the case of long sessions when there exists drift in user interests, and when there are not enough data to correctly model different items during training. Our study suggests that using the hybrid models of different approaches combined with baseline algorithms could lead to substantial results in session-based recommendations based on dataset characteristics. We also discuss the drawbacks of current session-based recommendation algorithms and further open research directions in this field.
一旦用户能够在短时间内找到更多符合其兴趣的商品，电子商务服务的销售额就有望得到提升。因此，推荐系统已成为任何成功的电子商务服务的关键组成部分。尽管电子商务中可以使用各种推荐技术，但近年来，基于会话的推荐系统受到了相当多的关注。这种兴趣的增长源于对收集个性化用户行为数据的安全担忧，特别是由于最近的通用数据保护法规。在这项工作中，我们对基于会话的推荐中使用的最先进深度学习方法进行了全面评估。在基于会话的推荐中，推荐系统依靠用户在同一会话中产生的事件序列来预测和推荐其他更有可能与其偏好相关的商品。我们广泛的实验研究了基线技术（如最近邻和模式挖掘算法）和深度学习方法（如循环神经网络、图神经网络和基于注意力的网络）。我们的评估表明，在大多数情况下，先进的基于神经网络的模型和基于会话的最近邻算法优于基线技术。然而，我们发现，当用户兴趣存在漂移，以及在训练期间没有足够的数据来正确建模不同商品时，这些模型在长会话情况下表现更差。我们的研究表明，根据数据集特征，使用不同方法的混合模型与基线算法相结合，可以在基于会话的推荐中取得显著成果。我们还讨论了当前基于会话的推荐算法的缺点以及该领域进一步的开放研究方向。
