Recommender systems play an important role in identifying and filtering relevant products based on the behaviours of users. Nevertheless, recommender systems suffer from the ‘cold-start’ problem, which occurs when no prior information about a new session or a user is available. Many approaches to solving the cold-start problem have been presented in the literature. However, there is still room for improving the performance of recommender systems in the cold-start stage. In this article, we present a novel method to alleviate the cold-start problem in session-based recommender systems. The purpose of this work is to develop a session similarity-based cold-start session alleviation approach for recommendation systems. The developed method uses previous sessions’ contextual and temporal features to find sessions similar to the newly started one. Our results on three different datasets show that, based on the provided Mean Average Precision and Normalised Discounted Cumulative Gain scores, the Session Similarity-based Framework consistently outperforms baseline models in terms of recommendation relevance and ranking quality across three used datasets. Our approach can be used to address the challenges associated with cold start sessions where no previously interacted items are present.

中文翻译：

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会话上下文数据集成，以解决电子商务推荐系统中的冷启动问题


推荐系统在根据用户行为识别和筛选相关产品方面发挥着重要作用。然而，推荐系统存在“冷启动”问题，当没有关于新会话或用户的先前信息可用时，就会出现这种情况。文献中已经介绍了许多解决冷启动问题的方法。但是，在冷启动阶段，推荐系统的性能仍有改进的空间。在本文中，我们提出了一种新方法来缓解基于会话的推荐系统中的冷启动问题。这项工作的目的是为推荐系统开发一种基于会话相似性的冷启动会话缓解方法。开发的方法使用先前会话的上下文和时间特征来查找与新启动的会话相似的会话。我们在三个不同数据集上的结果表明，根据提供的均值平均精度和标准化折扣累积增益分数，基于会话相似性的框架在三个使用的数据集的推荐相关性和排名质量方面始终优于基线模型。我们的方法可用于解决与冷启动会话相关的挑战，因为冷启动会话中不存在以前交互的项目。