# Memory Augmented Neural Model for Incremental Session-based Recommendation

arXiv: https://arxiv.org/abs/2005.01573 · HTML: https://mlsanity.com/paper/2005.01573/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [A Memory Transformer Network for Incremental Learning](https://mlsanity.com/paper/2210.04485/) (2022)
- [Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation](https://mlsanity.com/paper/2107.05124/) (2021)
- [GPT4Rec: Graph Prompt Tuning for Streaming Recommendation](https://mlsanity.com/paper/2406.08229/) (2024)
- [Rethinking Nearest Neighbors for Visual Classification](https://mlsanity.com/paper/2112.08459/) (2021)
- [Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts](https://mlsanity.com/paper/2508.05993/) (2025)
- [Better Late Than Never: Formulating and Benchmarking Recommendation Editing](https://mlsanity.com/paper/2406.04553/) (2024)
- [Representation Memorization for Fast Learning New Knowledge without Forgetting](https://mlsanity.com/paper/2108.12596/) (2021)
- [Feature Staleness Aware Incremental Learning for CTR Prediction](https://mlsanity.com/paper/2505.02844/) (2025)

## Abstract (arXiv)

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.