Generative Recommendation with Continuous-Token Diffusion

• URL: http://arxiv.org/abs/2504.12007v1
• Date: Wed, 16 Apr 2025 12:01:03 GMT
• Title: Generative Recommendation with Continuous-Token Diffusion
• Authors: Haohao Qu, Wenqi Fan, Shanru Lin,
• Abstract summary: We propose a novel framework for large language model (LLM)-based recommender systems (RecSys)<n>DeftRec incorporates textbfdenoising ditextbfffusion models to enable LLM-based RecSys to seamlessly support continuous textbftoken as input and target.<n>Given a continuous token as output, recommendations can be easily generated through score-based retrieval.
• Score: 11.23267167046234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, there has been a significant trend toward using large language model (LLM)-based recommender systems (RecSys). Current research primarily focuses on representing complex user-item interactions within a discrete space to align with the inherent discrete nature of language models. However, this approach faces limitations due to its discrete nature: (i) information is often compressed during discretization; (ii) the tokenization and generation for the vast number of users and items in real-world scenarios are constrained by a limited vocabulary. Embracing continuous data presents a promising alternative to enhance expressive capabilities, though this approach is still in its early stages. To address this gap, we propose a novel framework, DeftRec, which incorporates \textbf{de}noising di\textbf{f}fusion models to enable LLM-based RecSys to seamlessly support continuous \textbf{t}oken as input and target. First, we introduce a robust tokenizer with a masking operation and an additive K-way architecture to index users and items, capturing their complex collaborative relationships into continuous tokens. Crucially, we develop a denoising diffusion model to process user preferences within continuous domains by conditioning on reasoning content from pre-trained large language model. During the denoising process, we reformulate the objective to include negative interactions, building a comprehensive understanding of user preferences for effective and accurate recommendation generation. Finally, given a continuous token as output, recommendations can be easily generated through score-based retrieval. Extensive experiments demonstrate the effectiveness of the proposed methods, showing that DeftRec surpasses competitive benchmarks, including both traditional and emerging LLM-based RecSys.

Related papers

• Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Sampling [90.86991492288487]
  evaluating constraint on every token can be prohibitively expensive. LCD can distort the global distribution over strings, sampling tokens based only on local information. We show that our approach is superior to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-04-07T18:30:18Z)
• Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations [22.48125906976824]
  We introduce the Cascaded Organized Bi-Represented generAtive retrieval framework, which integrates sparse semantic IDs and dense vectors through a cascading process.<n>Our method alternates between generating these representations by first generating sparse IDs, which serve as conditions to aid in the generation of dense vectors.<n>During inference, COBRA employs a coarse-to-fine strategy, starting with sparse ID generation and refining them into dense vectors via the generative model.
  arXiv  Detail & Related papers  (2025-03-04T10:00:05Z)
• Few-Shot, No Problem: Descriptive Continual Relation Extraction [27.296604792388646]
  Few-shot Continual Relation Extraction is a crucial challenge for enabling AI systems to identify and adapt to evolving relationships in real-world domains. Traditional memory-based approaches often overfit to limited samples, failing to reinforce old knowledge. We propose a novel retrieval-based solution, starting with a large language model to generate descriptions for each relation.
  arXiv  Detail & Related papers  (2025-02-27T23:44:30Z)
• Enhancing Item Tokenization for Generative Recommendation through Self-Improvement [67.94240423434944]
  Generative recommendation systems are driven by large language models (LLMs)<n>Current item tokenization methods include using text descriptions, numerical strings, or sequences of discrete tokens.<n>We propose a self-improving item tokenization method that allows the LLM to refine its own item tokenizations during training process.
  arXiv  Detail & Related papers  (2024-12-22T21:56:15Z)
• KL-geodesics flow matching with a novel sampling scheme [4.347494885647007]
  Non-autoregressive language models generate all tokens simultaneously, offering potential speed advantages over traditional autoregressive models.<n>We investigate a conditional flow matching approach for text generation.
  arXiv  Detail & Related papers  (2024-11-25T17:15:41Z)
• 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)
• ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation [38.64175351885443]
  Large language models have been flourishing in the natural language processing (NLP) domain. Despite the intelligence shown by the recommendation-oriented finetuned models, LLMs struggle to fully understand the user behavior patterns. Existing works only fine-tune a sole LLM on given text data without introducing that important information to it.
  arXiv  Detail & Related papers  (2024-06-27T01:37:57Z)
• Diffusion-based Contrastive Learning for Sequential Recommendation [6.3482831836623355]
  We propose a Context-aware Diffusion-based Contrastive Learning for Sequential Recommendation, named CaDiRec.<n>CaDiRec employs a context-aware diffusion model to generate alternative items for the given positions within a sequence.<n>We train the entire framework in an end-to-end manner, with shared item embeddings between the diffusion model and the recommendation model.
  arXiv  Detail & Related papers  (2024-05-15T14:20:37Z)
• Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency [71.42261918225773]
  Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text. As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
  arXiv  Detail & Related papers  (2023-05-31T16:47:20Z)
• Sequential Recommendation via Stochastic Self-Attention [68.52192964559829]
  Transformer-based approaches embed items as vectors and use dot-product self-attention to measure the relationship between items. We propose a novel textbfSTOchastic textbfSelf-textbfAttention(STOSA) to overcome these issues. We devise a novel Wasserstein Self-Attention module to characterize item-item position-wise relationships in sequences.
  arXiv  Detail & Related papers  (2022-01-16T12:38:45Z)
• Contrastive Self-supervised Sequential Recommendation with Robust Augmentation [101.25762166231904]
  Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. Old and new issues remain, including data-sparsity and noisy data. We propose Contrastive Self-Supervised Learning for sequential Recommendation (CoSeRec)
  arXiv  Detail & Related papers  (2021-08-14T07:15:25Z)

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.