# DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models arXiv: https://arxiv.org/abs/2408.12153 ยท HTML: https://mlsanity.com/paper/2408.12153/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Placed in Diffusion recommenders, one of 14 directions in Diffusion models. - Builds on 22 of its 30 references that are in the map. - First posted on arXiv 22 Aug 2024. ## Placed in - Diffusion models / [Diffusion recommenders](https://mlsanity.com/diffusion/diffusion-recommenders/) ## Builds on (most cited first, 22 of 22) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016) - [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018) - [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019) - [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016) - [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015) - [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017) - [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018) - [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017) - [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018) - [Riemannian Score-Based Generative Modelling](https://mlsanity.com/paper/2202.02763/) (2022) - [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016) - [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://mlsanity.com/paper/1904.08030/) (2019) - [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020) - [DiffuRec: A Diffusion Model for Sequential Recommendation](https://mlsanity.com/paper/2304.00686/) (2023) - [On Using Very Large Target Vocabulary for Neural Machine Translation](https://mlsanity.com/paper/1412.2007/) (2014) - [Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion](https://mlsanity.com/paper/2310.20453/) (2023) - [Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation](https://mlsanity.com/paper/2103.10693/) (2021) - [Sequential Recommendation with Self-Attentive Multi-Adversarial Network](https://mlsanity.com/paper/2005.10602/) (2020) - [Discrete Conditional Diffusion for Reranking in Recommendation](https://mlsanity.com/paper/2308.06982/) (2023) ## Built on by (most cited first, 22 of 22) - [A Survey on Diffusion Models for Recommender Systems](https://mlsanity.com/paper/2409.05033/) (2024) - [LLaDA-Rec: Discrete Diffusion for Parallel Semantic ID Generation in Generative Recommendation](https://mlsanity.com/paper/2511.06254/) (2025) - [Dual Conditional Diffusion Models for Sequential Recommendation](https://mlsanity.com/paper/2410.21967/) (2024) - [Masked Diffusion for Generative Recommendation](https://mlsanity.com/paper/2511.23021/) (2025) - [KuaiFormer: Transformer-Based Retrieval at Kuaishou](https://mlsanity.com/paper/2411.10057/) (2024) - [S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain](https://mlsanity.com/paper/2501.00384/) (2024) - [Addressing Missing Data Issue for Diffusion-based Recommendation](https://mlsanity.com/paper/2505.12283/) (2025) - [Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach](https://mlsanity.com/paper/2505.19544/) (2025) - [FlowRec: Prior-Informed Flow Matching for Efficient Sequential Recommendation Generation](https://mlsanity.com/paper/2508.17618/) (2025) - [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025) - [Flow Matching based Sequential Recommender Model](https://mlsanity.com/paper/2505.16298/) (2025) - [Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation](https://mlsanity.com/paper/2509.26063/) (2025) - [On Efficiency-Effectiveness Trade-off of Diffusion-based Recommenders](https://mlsanity.com/paper/2510.17245/) (2025) - [CRM: Retrieval Model with Controllable Condition](https://mlsanity.com/paper/2412.13844/) (2024) - [Brownian Bridge Diffusion for Sequential Recommendation](https://mlsanity.com/paper/2507.06121/) (2025) - [SimDiffRec: Semantic Similarity-Guided Diffusion for Contrastive Sequential Recommendation](https://mlsanity.com/paper/2507.11866/) (2025) - [Generative Diffusion Augmentation with Quantum-Enhanced Discrimination for Medical Image Diagnosis](https://mlsanity.com/paper/2601.18556/) (2026) - [From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2602.23132/) (2026) - [FlowTime: Towards Continuous Generative Watch Time Prediction via Flow-based Personalized Priors](https://mlsanity.com/paper/2606.01352/) (2026) - [Generative Retrieval via Diffusion Transformer with Metric-Ordered Sequence Training and Hybrid-Policy Preference Optimization](https://mlsanity.com/paper/2606.26899/) (2026) - [Adaptive Item-based Collaborative Structures via Noise Rescheduling in Diffusion for Generative Recommendation](https://mlsanity.com/paper/2608.23400/) (2026) - [MGDiff: Multi-Interest Sequence Recommendation with Masking GNN-Guided Diffusion](https://mlsanity.com/paper/2609.01619/) (2026) ## Abstract (arXiv) Sequential Recommendation (SR) plays a pivotal role in recommender systems by tailoring recommendations to user preferences based on their non-stationary historical interactions. Achieving high-quality performance in SR requires attention to both item representation and diversity. However, designing an SR method that simultaneously optimizes these merits remains a long-standing challenge. In this study, we address this issue by integrating recent generative Diffusion Models (DM) into SR. DM has demonstrated utility in representation learning and diverse image generation. Nevertheless, a straightforward combination of SR and DM leads to sub-optimal performance due to discrepancies in learning objectives (recommendation vs. noise reconstruction) and the respective learning spaces (non-stationary vs. stationary). To overcome this, we propose a novel framework called DimeRec (Diffusion with multi-interest enhanced Recommender). DimeRec synergistically combines a guidance extraction module (GEM) and a generative diffusion aggregation module (DAM). The GEM extracts crucial stationary guidance signals from the user's non-stationary interaction history, while the DAM employs a generative diffusion process conditioned on GEM's outputs to reconstruct and generate consistent recommendations. Our numerical experiments demonstrate that DimeRec significantly outperforms established baseline methods across three publicly available datasets. Furthermore, we have successfully deployed DimeRec on a large-scale short video recommendation platform, serving hundreds of millions of users. Live A/B testing confirms that our method improves both users' time spent and result diversification.