Non-autoregressive Generative Models for Reranking Recommendation

• URL: http://arxiv.org/abs/2402.06871v4
• Date: Tue, 20 Aug 2024 11:29:37 GMT
• Title: Non-autoregressive Generative Models for Reranking Recommendation
• Authors: Yuxin Ren, Qiya Yang, Yichun Wu, Wei Xu, Yalong Wang, Zhiqiang Zhang,
• Abstract summary: In a recommendation system, reranking plays a crucial role by modeling the intra-list correlations among items. We propose a Non-AutoRegressive generative model for reranking Recommendation (NAR4Rec) designed to enhance efficiency and effectiveness. NAR4Rec has been fully deployed in a popular video app Kuaishou with over 300 million daily active users.
• Score: 9.854541524740549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contemporary recommendation systems are designed to meet users' needs by delivering tailored lists of items that align with their specific demands or interests. In a multi-stage recommendation system, reranking plays a crucial role by modeling the intra-list correlations among items. The key challenge of reranking lies in the exploration of optimal sequences within the combinatorial space of permutations. Recent research proposes a generator-evaluator learning paradigm, where the generator generates multiple feasible sequences and the evaluator picks out the best sequence based on the estimated listwise score. The generator is of vital importance, and generative models are well-suited for the generator function. Current generative models employ an autoregressive strategy for sequence generation. However, deploying autoregressive models in real-time industrial systems is challenging. To address these issues, we propose a Non-AutoRegressive generative model for reranking Recommendation (NAR4Rec) designed to enhance efficiency and effectiveness. To tackle challenges such as sparse training samples and dynamic candidates, we introduce a matching model. Considering the diverse nature of user feedback, we employ a sequence-level unlikelihood training objective to differentiate feasible sequences from unfeasible ones. Additionally, to overcome the lack of dependency modeling in non-autoregressive models regarding target items, we introduce contrastive decoding to capture correlations among these items. Extensive offline experiments validate the superior performance of NAR4Rec over state-of-the-art reranking methods. Online A/B tests reveal that NAR4Rec significantly enhances the user experience. Furthermore, NAR4Rec has been fully deployed in a popular video app Kuaishou with over 300 million daily active users.

Related papers

• Generative Diffusion Models for Sequential Recommendations [7.948486055890262]
  Generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have shown promise in sequential recommendation tasks. This research introduces enhancements to the DiffuRec architecture to improve robustness and incorporates a cross-attention mechanism in the Approximator to better capture relevant user-item interactions.
  arXiv  Detail & Related papers  (2024-10-25T09:39:05Z)
• GenRec: Generative Sequential Recommendation with Large Language Models [4.381277509913139]
  We propose a novel model named Generative Recommendation (GenRec) GenRec is lightweight and requires only a few hours to train effectively in low-resource settings. Our experiments have demonstrated that GenRec generalizes on various public real-world datasets.
  arXiv  Detail & Related papers  (2024-07-30T20:58:36Z)
• Diffusion Augmentation for Sequential Recommendation [47.43402785097255]
  We propose a Diffusion Augmentation for Sequential Recommendation (DiffuASR) for a higher quality generation. The augmented dataset by DiffuASR can be used to train the sequential recommendation models directly, free from complex training procedures. We conduct extensive experiments on three real-world datasets with three sequential recommendation models.
  arXiv  Detail & Related papers  (2023-09-22T13:31:34Z)
• MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation [61.45986275328629]
  We propose MISSRec, a multi-modal pre-training and transfer learning framework for sequential recommendation. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation.
  arXiv  Detail & Related papers  (2023-08-22T04:06:56Z)
• SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking [60.109453252858806]
  A maximum-likelihood (MLE) objective does not match a downstream use-case of autoregressively generating high-quality sequences. We formulate sequence generation as an imitation learning (IL) problem. This allows us to minimize a variety of divergences between the distribution of sequences generated by an autoregressive model and sequences from a dataset. Our resulting method, SequenceMatch, can be implemented without adversarial training or architectural changes.
  arXiv  Detail & Related papers  (2023-06-08T17:59:58Z)
• Conditional Denoising Diffusion for Sequential Recommendation [62.127862728308045]
  Two prominent generative models, Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) GANs suffer from unstable optimization, while VAEs are prone to posterior collapse and over-smoothed generations. We present a conditional denoising diffusion model, which includes a sequence encoder, a cross-attentive denoising decoder, and a step-wise diffuser.
  arXiv  Detail & Related papers  (2023-04-22T15:32:59Z)
• Generative Slate Recommendation with Reinforcement Learning [49.75985313698214]
  reinforcement learning algorithms can be used to optimize user engagement in recommender systems. However, RL approaches are intractable in the slate recommendation scenario. In that setting, an action corresponds to a slate that may contain any combination of items. In this work we propose to encode slates in a continuous, low-dimensional latent space learned by a variational auto-encoder. We are able to (i) relax assumptions required by previous work, and (ii) improve the quality of the action selection by modeling full slates.
  arXiv  Detail & Related papers  (2023-01-20T15:28:09Z)
• DORE: Document Ordered Relation Extraction based on Generative Framework [56.537386636819626]
  This paper investigates the root cause of the underwhelming performance of the existing generative DocRE models. We propose to generate a symbolic and ordered sequence from the relation matrix which is deterministic and easier for model to learn. Experimental results on four datasets show that our proposed method can improve the performance of the generative DocRE models.
  arXiv  Detail & Related papers  (2022-10-28T11:18:10Z)
• 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)
• Sequence Adaptation via Reinforcement Learning in Recommender Systems [8.909115457491522]
  We propose the SAR model, which learns the sequential patterns and adjusts the sequence length of user-item interactions in a personalized manner. In addition, we optimize a joint loss function to align the accuracy of the sequential recommendations with the expected cumulative rewards of the critic network. Our experimental evaluation on four real-world datasets demonstrates the superiority of our proposed model over several baseline approaches.
  arXiv  Detail & Related papers  (2021-07-31T13:56:46Z)
• Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation [25.37244686572865]
  We propose a novel method called Adversarial and Contrastive Variational Autoencoder (ACVAE) for sequential recommendation. We first introduce the adversarial training for sequence generation under the Adversarial Variational Bayes framework, which enables our model to generate high-quality latent variables. Besides, when encoding the sequence, we apply a recurrent and convolutional structure to capture global and local relationships in the sequence.
  arXiv  Detail & Related papers  (2021-03-19T09:01:14Z)

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.