Analyzing sequences of interactions between users and items, sequential recommendation models can learn user intent and make predictions about the next item. Next to item interactions, most systems also have interactions with what we call non-item pages: these pages are not related to specific items but still can provide insights of the user's interests, as, for example, navigation pages. We therefore propose a general way to include these non-item pages in sequential recommendation models to enhance next-item prediction. First, we demonstrate the influence of non-item pages on following interactions with the hypotheses testing framework HypTrails and propose methods for representing non-item pages in sequential recommendation models. Subsequently, we adapt popular sequential recommender models to integrate non-item pages and investigate their performance with different item representation strategies as well as their ability to handle noisy data. To show the general capabilities of the models to integrate non-item pages, we create a synthetic dataset for a controlled setting and then evaluate the improvements from including non-item pages on two real-world datasets. Our results show that non-item pages are a valuable source of information, and incorporating them in sequential recommendation models increases the performance of next-item prediction across all analyzed model architectures.

翻译：通过分析用户与项目之间的交互序列，序列化推荐模型能够学习用户意图并预测下一项目。除了项目交互外，大多数系统还存在我们称之为非项目页面的交互：这些页面与特定项目无关，但仍能提供用户兴趣的洞察，例如导航页面。因此，我们提出一种通用方法，将非项目页面纳入序列化推荐模型以增强下一项目预测。首先，我们利用假设检验框架HypTrails验证非项目页面对后续交互的影响，并提出在序列化推荐模型中表示非项目页面的方法。随后，我们改进主流序列化推荐模型以整合非项目页面，研究其在不同项目表示策略下的性能及处理噪声数据的能力。为验证模型整合非项目页面的通用能力，我们创建了受控环境下的合成数据集，并在两个真实数据集上评估纳入非项目页面带来的改进。结果表明，非项目页面是宝贵的信息源，将其纳入序列化推荐模型能全面提升所有分析模型架构的下一项目预测性能。