论文标题
核心：在一致表示空间内简单有效的基于会话的建议
CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space
论文作者
论文摘要
基于会话的建议（SBR）是指根据匿名会话中的短期用户行为预测下一个项目的任务。但是，非线性编码器学到的会话嵌入通常与项目嵌入的表示空间相同，从而导致预测问题不一致。为了解决此问题，我们提出了一个名为Core的简单有效框架，该框架可以统一编码和解码过程的表示空间。首先，我们设计了一个符合表示的编码器，该编码器将输入项目嵌入的线性组合作为会话嵌入，以确保会话和项目在相同的表示空间中。此外，我们提出了一种可靠的距离测量方法，以防止在一致表示空间中过度拟合嵌入。在五个公共现实世界数据集上进行的广泛实验证明了该方法的有效性和效率。该代码可在以下网址获得：https：//github.com/rucaibox/core。
Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session. However, session embedding learned by a non-linear encoder is usually not in the same representation space as item embeddings, resulting in the inconsistent prediction issue while recommending items. To address this issue, we propose a simple and effective framework named CORE, which can unify the representation space for both the encoding and decoding processes. Firstly, we design a representation-consistent encoder that takes the linear combination of input item embeddings as session embedding, guaranteeing that sessions and items are in the same representation space. Besides, we propose a robust distance measuring method to prevent overfitting of embeddings in the consistent representation space. Extensive experiments conducted on five public real-world datasets demonstrate the effectiveness and efficiency of the proposed method. The code is available at: https://github.com/RUCAIBox/CORE.
