论文标题
基于增量会话建议的内存增强神经模型
Memory Augmented Neural Model for Incremental Session-based Recommendation
论文作者
论文摘要
除了当前浏览器会话中观察到的内容外，对隐私的越来越关注激发了基于会话建议（SR）的兴趣。现有方法是在现实世界应用中很少发生的静态设置中评估的。为了更好地解决SR任务的动态性质，我们研究了一个增量SR方案，新项目和偏好不断出现。我们表明，现有的神经推荐器可用于增量SR方案，并具有较小的增量更新，以减轻计算开销和灾难性遗忘。更重要的是，我们提出了一个称为记忆增强神经模型（MAN）的通用框架。人增强了一个基本的神经推荐剂，具有连续查询和更新的非参数内存，并通过另一个轻量级的门控网络组合了神经和内存组件的预测。我们从经验上表明，Man非常适合增量SR任务，并且它始终优于最先进的神经和非参数方法。我们分析结果并证明它在对新项目和不经常的项目上逐步学习偏好方面特别擅长。
Increasing concerns with privacy have stimulated interests in Session-based Recommendation (SR) using no personal data other than what is observed in the current browser session. Existing methods are evaluated in static settings which rarely occur in real-world applications. To better address the dynamic nature of SR tasks, we study an incremental SR scenario, where new items and preferences appear continuously. We show that existing neural recommenders can be used in incremental SR scenarios with small incremental updates to alleviate computation overhead and catastrophic forgetting. More importantly, we propose a general framework called Memory Augmented Neural model (MAN). MAN augments a base neural recommender with a continuously queried and updated nonparametric memory, and the predictions from the neural and the memory components are combined through another lightweight gating network. We empirically show that MAN is well-suited for the incremental SR task, and it consistently outperforms state-of-the-art neural and nonparametric methods. We analyze the results and demonstrate that it is particularly good at incrementally learning preferences on new and infrequent items.
