For present e-commerce platforms, session-based recommender systems are developed to predict users' preference for next-item recommendation. Although a session can usually reflect a user's current preference, a local shift of the user's intention within the session may still exist. Specifically, the interactions that take place in the early positions within a session generally indicate the user's initial intention, while later interactions are more likely to represent the latest intention. Such positional information has been rarely considered in existing methods, which restricts their ability to capture the significance of interactions at different positions. To thoroughly exploit the positional information within a session, a theoretical framework is developed in this paper to provide an in-depth analysis of the positional information. We formally define the properties of forward-awareness and backward-awareness to evaluate the ability of positional encoding schemes in capturing the initial and the latest intention. According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session. To enhance the positional encoding scheme for the session-based recommendation, a dual positional encoding (DPE) is proposed to account for both forward-awareness and backward-awareness. Based on DPE, we propose a novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module to fully exploit the positional information for session-based recommendation tasks. Extensive experiments are conducted on two e-commerce benchmark datasets, Yoochoose and Diginetica and the experimental results show the superiority of the PosRec by comparing it with the state-of-the-art session-based recommender models.

中文翻译：

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利用位置信息进行基于会话的推荐

对于目前的电子商务平台，基于会话的推荐系统被开发来预测用户对下一个项目推荐的偏好。虽然会话通常可以反映用户当前的偏好，但是会话内用户意图的局部转移可能仍然存在。具体而言，在会话中发生在较早位置的交互通常表示用户的初始意图，而较晚的交互更可能代表最近的意图。现有方法很少考虑此类位置信息，这限制了它们捕捉不同位置交互重要性的能力。为了彻底利用会话中的位置信息，本文开发了一个理论框架，以提供对位置信息的深入分析。我们正式定义了前向意识和后向意识的属性，以评估位置编码方案在捕获初始和最新意图方面的能力。根据我们的分析，现有的位置编码方案通常仅具有前向感知能力，很难代表会话中意图的动态。为了增强基于会话的推荐的位置编码方案，提出了双重位置编码 (DPE) 来考虑前向感知和后向感知。基于 DPE，我们提出了一种新颖的位置推荐器 (PosRec) 模型，该模型具有精心设计的位置感知门控图神经网络模块，以充分利用基于会话的推荐任务的位置信息。在两个电子商务基准数据集 Yoochoose 和 Diginetica 上进行了广泛的实验，实验结果通过将 PosRec 与最先进的基于会话的推荐模型进行比较来证明其优越性。