Masked Diffusion for Generative Recommendation

• URL: http://arxiv.org/abs/2511.23021v1
• Date: Fri, 28 Nov 2025 09:36:26 GMT
• Title: Masked Diffusion for Generative Recommendation
• Authors: Kulin Shah, Bhuvesh Kumar, Neil Shah, Liam Collins,
• Abstract summary: Generative recommendation (GR) with semantic IDs (SIDs) has emerged as a promising alternative to traditional recommendation approaches.<n>We propose to instead model and learn the probability of a user's sequence of SIDs using masked diffusion.<n>We demonstrate through thorough experiments that our proposed method consistently outperforms autoregressive modeling.
• Score: 30.8737219110446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative recommendation (GR) with semantic IDs (SIDs) has emerged as a promising alternative to traditional recommendation approaches due to its performance gains, capitalization on semantic information provided through language model embeddings, and inference and storage efficiency. Existing GR with SIDs works frame the probability of a sequence of SIDs corresponding to a user's interaction history using autoregressive modeling. While this has led to impressive next item prediction performances in certain settings, these autoregressive GR with SIDs models suffer from expensive inference due to sequential token-wise decoding, potentially inefficient use of training data and bias towards learning short-context relationships among tokens. Inspired by recent breakthroughs in NLP, we propose to instead model and learn the probability of a user's sequence of SIDs using masked diffusion. Masked diffusion employs discrete masking noise to facilitate learning the sequence distribution, and models the probability of masked tokens as conditionally independent given the unmasked tokens, allowing for parallel decoding of the masked tokens. We demonstrate through thorough experiments that our proposed method consistently outperforms autoregressive modeling. This performance gap is especially pronounced in data-constrained settings and in terms of coarse-grained recall, consistent with our intuitions. Moreover, our approach allows the flexibility of predicting multiple SIDs in parallel during inference while maintaining superior performance to autoregressive modeling.

Related papers

• Unleash the Potential of Long Semantic IDs for Generative Recommendation [5.6264583086973685]
  ACERec is a novel framework that decouples the gap between fine-grained tokenization and efficient sequential modeling.<n>It consistently outperforms state-of-the-art baselines on six real-world benchmarks.
  arXiv  Detail & Related papers  (2026-02-14T03:15:31Z)
• Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs [17.944727019161878]
  ReSID is a principled, SID framework that recommend learning from the perspective of information preservation and sequential predictability.<n>It consistently outperforms strong sequential and SID-based generative baselines by an average of over 10%, while reducing tokenization cost by up to 122x.
  arXiv  Detail & Related papers  (2026-02-02T17:00:04Z)
• Auto-Regressive Masked Diffusion Models [9.239507801466322]
  Masked diffusion models (MDMs) have emerged as a promising approach for language modeling.<n>They face a performance gap compared to autoregressive models (ARMs) and require more training iterations.<n>We present the Auto-Regressive Masked Diffusion model, which unifies the training efficiency of autoregressive models with the parallel generation capabilities of diffusion-based models.
  arXiv  Detail & Related papers  (2026-01-23T18:42:30Z)
• Learning Unmasking Policies for Diffusion Language Models [33.44995119635116]
  Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks.<n>One particularly successful variant is masked discrete diffusion, in which a buffer filled with special mask tokens is progressively replaced with tokens sampled from the model's vocabulary.<n>In this work, we propose to train sampling procedures using reinforcement learning.
  arXiv  Detail & Related papers  (2025-12-09T20:44:33Z)
• DINGO: Constrained Inference for Diffusion LLMs [5.971462597321995]
  Diffusion models lack the ability to provably enforce user-specified formal constraints.<n>We propose DINGO, a dynamic programming-based decoding strategy that is both efficient and provably distribution-preserving.
  arXiv  Detail & Related papers  (2025-05-29T04:04:54Z)
• Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding [55.2480439325792]
  In arbitrary-order language models, it is an open question how to sample tokens in parallel from the correct joint distribution.<n>We find that a different class of models, any-subset autoregressive models (AS-ARMs), holds the solution.<n>We show that AS-ARMs achieve state-of-the-art performance among sub-200M parameter models on infilling benchmark tasks, and nearly match the performance of models 50X larger on code generation.
  arXiv  Detail & Related papers  (2025-04-29T06:33:13Z)
• Diffusion Generative Recommendation with Continuous Tokens [21.222713476105195]
  ContRec is a framework that seamlessly integrates continuous tokens into LLM-based RecSys.<n>We show that ContRec consistently outperforms both traditional and SOTA LLM-based recommender systems.<n>Our results highlight the potential of continuous tokenization and generative modeling for advancing the next generation of recommender systems.
  arXiv  Detail & Related papers  (2025-04-16T12:01:03Z)
• Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model [66.91323540178739]
  Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior. We revisit SR from a novel information-theoretic perspective and find that sequential modeling methods fail to adequately capture randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests.
  arXiv  Detail & Related papers  (2024-10-31T14:52:01Z)
• Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration [54.897493351694195]
  We propose a novel parallel decoding approach, namely textithidden transfer, which decodes multiple successive tokens simultaneously in a single forward pass. In terms of acceleration metrics, we outperform all the single-model acceleration techniques, including Medusa and Self-Speculative decoding.
  arXiv  Detail & Related papers  (2024-04-18T09:17:06Z)
• Non-autoregressive Sequence-to-Sequence Vision-Language Models [59.445765313094434]
  We propose a parallel decoding sequence-to-sequence vision-language model that marginalizes over multiple inference paths in the decoder.<n>The model achieves performance on-par with its state-of-the-art autoregressive counterpart, but is faster at inference time.
  arXiv  Detail & Related papers  (2024-03-04T17:34:59Z)
• Amortizing intractable inference in large language models [56.92471123778389]
  We use amortized Bayesian inference to sample from intractable posterior distributions. We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem.
  arXiv  Detail & Related papers  (2023-10-06T16:36:08Z)
• Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck [52.08901549360262]
  Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. VAEs tend to ignore latent variables with a strong auto-regressive decoder. We propose a principled approach to enforce an implicit latent feature matching in a more compact latent space.
  arXiv  Detail & Related papers  (2020-04-22T14:41:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.