Session-based Recommendation Algorithm Based on Memory Augmented Network

doi:10.19678/j.issn.1000-3428.0068444

Abstract

Abstract:

Session-based recommendation systems serve as essential tools for assisting users in identifying matching interests and requirements from large volumes of data. These systems aim to predict the next user actions based on anonymous sessions. However, existing methods inadequately represent the overall interests of a user and frequently neglect the positional relationships among items. To address this limitation, an enhanced memory network-based session recommendation model, SR-MAN, is proposed to analyze global user interest representations and item sequence problems. Initially, the method introduces position encoding during the generation of item embedding vectors to emphasize the impact of different positions on the sequence. Subsequently, a neural Turing machine is employed to store recent session information, and an attention network is designed to learn long-term user preferences by integrating the most recent user interaction as the current interest indicator. Finally, the method integrates long-term and current preferences to predict and recommend items of interest. Bayesian Personalized Ranking(BPR) is employed to estimate the model parameters. Experiments on three datasets demonstrate the effectiveness of the proposed method.

Key words: session-based recommendation, memory network, position encoding, attention network, interest extraction

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。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, 凸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, 使用贝叶斯个性化排序(BPR)来估计模型参数, 并在3个数据集上的实验验证了所提方法的有效性。

关键词: 会话推荐, 记忆网络, 位置编码, 注意力网络, 兴趣提取

WEI Xing, SUN Hao, CAO Jian, ZHU Xiaobin. Session-based Recommendation Algorithm Based on Memory Augmented Network[J]. Computer Engineering, 2024, 50(12): 83-89.

魏星, 孙浩, 曹健, 祝晓斌. 基于增强记忆网络的会话推荐算法[J]. 计算机工程, 2024, 50(12): 83-89.

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