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.

本文提出了一种称为MAN的框架，该框架通过增加基本神经推荐器的不断查询和更新的非参数存储器，并通过另一个轻量级门控网络将神经和记忆组件的预测进行组合，从而适用于不断增加的SR任务，并在实验中证明其超越了当前最先进的神经和非参数化方法，特别是对于新和不常用物品的学习。

增量会话推荐的记忆增强神经模型