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.
翻译:对于目前的电子商务平台,制定基于届会的建议系统,以预测用户对下个项目建议的偏好。虽然届会通常可以反映用户当前的偏好,但会中用户意图的局部转变可能仍然存在。具体地说,届会早期位置上的互动一般表明用户的初衷,而后来的互动更可能代表最新意向。在现有方法中,这种定位信息很少被考虑,这限制了他们捕捉不同职位互动重要性的能力。为了充分利用届会中的位置信息,本文件开发了一个理论框架,对定位信息进行深入分析。我们正式界定了前意识和后意识的特性,以评价定位编码计划在获取初始和最新意向方面的能力。根据我们的分析,现有的定位编码计划一般只有前意识,这很难代表届会中意图的动态。为了加强届会建议基于位置的编码计划,本文件中提出了双向位置编码(DPE),以对定位信息定位信息的深度进行深入分析。我们正式界定了前认识和后意识系统模型,并提出了关于前向方向和后意识的动态数据库。