Abstract: As a key tool to assist users in finding matching interests and requirements from massive amounts of data, the goal of session-based recommendation systems is to predict a user's next actions based on anonymous sessions. Existing methods have insufficient representation of users' overall interests and rarely consider the positional relationship among items. Therefore, an enhanced memory network-based session recommendation model, SR-MAN, was proposed to analyze global user interest representation and item sequence problems. Initially, the method introduced position encoding while generating the item embedding vector to highlight the influence of different positions on the sequence. Subsequently, the neural Turing machine was employed to store recent session information, and an attention network was designed to learn long-term preferences, combining users' last click as the current interest preference. Finally, the method integrated long-term and current preferences for prediction and recommended items of interest. Bayesian personalized ranking was employed to estimate the model parameters. Experiments on three datasets confirmed the effectiveness of the proposed method.

摘要： 作为协助用户从海量数据中找到匹配兴趣和需求内容的关键，会话推荐系统的目标是基于匿名会话预测用户的下一个行为。目前常见的推荐算法对于用户整体兴趣表示不足，而且很少考虑物品间的位置关系。本文提出一种基于增强记忆网络的会话推荐模型SR-MAN，旨在分析全局用户兴趣表征和物品顺序问题。首先，在物品嵌入向量生成时引入位置编码，突显不同位置对序列的影响，再借助神经图灵机存储近期会话信息，并设计了注意力网络学习长期偏好，结合用户末次点击作为当前兴趣偏好。最后，通过整合长期与当前偏好进行预测，推荐用户感兴趣的项目。在算法训练的过程中，使用了贝叶斯个性化排名来估计模型参数，并在三个数据集上的实验验证了本方法的有效性。