论文标题
基于会话的k-nns具有针对下一项目预测的语义建议
Session-based k-NNs with Semantic Suggestions for Next-item Prediction
论文作者
论文摘要
电子商务领域中最关键的问题之一是信息超载问题。通常，向用户提供大量产品。该领域的特征迫使研究人员选择基于会话的推荐方法，从中，基于邻骨的最接近的方法（SKNN）方法已证明与基于神经网络的模型具有竞争力，甚至均超过了基于神经网络的模型。但是，现有的SKNN方法缺乏检测微观层面突然兴趣变化的能力，即在单个会话中；并将他们的建议适应这些变化。在本文中，我们提出了一个概念（CSKNN）模型扩展，以扩展下一步预测，从而可以通过语义级别的属性更好地适应兴趣变化。我们使用NLP技术来解析产品标题的显着概念，以创建基于语言的产品概括，用于更改检测和推荐清单后进行过滤。我们对我们的扩展的两个版本进行了实验，这些实验在语义推导过程方面有所不同，同时在稀疏时尚电子商务数据集上对现有SKNN方法的改进都有所改善。
One of the most critical problems in e-commerce domain is the information overload problem. Usually, an enormous number of products is offered to a user. The characteristics of this domain force researchers to opt for session-based recommendation methods, from which nearest-neighbors-based (SkNN) approaches have been shown to be competitive with and even outperform neural network-based models. Existing SkNN approaches, however, lack the ability to detect sudden interest changes at a micro-level, i.e., during an individual session; and to adapt their recommendations to these changes. In this paper, we propose a conceptual (cSkNN) model extension for the next-item prediction allowing better adaptation to the interest changes via the semantic-level properties. We use an NLP technique to parse salient concepts from the product titles to create linguistically based product generalizations that are used for change detection and a recommendation list post-filtering. We conducted experiments with two versions of our extension that differ in semantics derivation procedure while both showing an improvement over the existing SkNN method on a sparse fashion e-commerce dataset.
