# Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation

arXiv: https://arxiv.org/abs/2109.00368 · HTML: https://mlsanity.com/paper/2109.00368/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2021.

## Built on by (most cited first, 16 of 16)

- [Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2402.14473/) (2024)
- [Effective and Efficient Training for Sequential Recommendation using Recency Sampling](https://mlsanity.com/paper/2207.02643/) (2022)
- [DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2209.10163/) (2022)
- [Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model](https://mlsanity.com/paper/2410.23994/) (2024)
- [Improving Conversational Recommendation Systems via Counterfactual Data Simulation](https://mlsanity.com/paper/2306.02842/) (2023)
- [Scaling Sequential Recommendation Models with Transformers](https://mlsanity.com/paper/2412.07585/) (2024)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender Systems](https://mlsanity.com/paper/2407.13163/) (2024)
- [ELECRec: Training Sequential Recommenders as Discriminators](https://mlsanity.com/paper/2204.02011/) (2022)
- [Generating Negative Samples for Sequential Recommendation](https://mlsanity.com/paper/2208.03645/) (2022)
- [RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation](https://mlsanity.com/paper/2309.10469/) (2023)
- [DARLR: Dual-Agent Offline Reinforcement Learning for Recommender Systems with Dynamic Reward](https://mlsanity.com/paper/2505.07257/) (2025)
- [SAICL: Student Modelling with Interaction-level Auxiliary Contrastive Tasks for Knowledge Tracing and Dropout Prediction](https://mlsanity.com/paper/2210.09012/) (2022)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)
- [Enhancing Recommendation with Denoising Auxiliary Task](https://mlsanity.com/paper/2409.17402/) (2024)
- [Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling](https://mlsanity.com/paper/2502.16239/) (2025)

## Abstract (arXiv)

The sequential recommendation aims to recommend items, such as products, songs and places, to users based on the sequential patterns of their historical records. Most existing sequential recommender models consider the next item prediction task as the training signal. Unfortunately, there are two essential challenges for these methods: (1) the long-term preference is difficult to capture, and (2) the supervision signal is too sparse to effectively train a model. In this paper, we propose a novel sequential recommendation framework to overcome these challenges based on a memory augmented multi-instance contrastive predictive coding scheme, denoted as MMInfoRec. The basic contrastive predictive coding (CPC) serves as encoders of sequences and items. The memory module is designed to augment the auto-regressive prediction in CPC to enable a flexible and general representation of the encoded preference, which can improve the ability to capture the long-term preference. For effective training of the MMInfoRec model, a novel multi-instance noise contrastive estimation (MINCE) loss is proposed, using multiple positive samples, which offers effective exploitation of samples inside a mini-batch. The proposed MMInfoRec framework falls into the contrastive learning style, within which, however, a further finetuning step is not required given that its contrastive training task is well aligned with the target recommendation task. With extensive experiments on four benchmark datasets, MMInfoRec can outperform the state-of-the-art baselines.