Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings

• URL: http://arxiv.org/abs/2409.05022v1
• Date: Sun, 8 Sep 2024 08:27:22 GMT
• Title: Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings
• Authors: Linsey Pang, Amir Hossein Raffiee, Wei Liu, Keld Lundgaard,
• Abstract summary: Sequential recommendation models have achieved state-of-the-art performance using self-attention mechanism. Moving beyond only using item ID and positional embeddings leads to a significant accuracy boost when predicting the next item. We introduce a mix-attention mechanism with a layer-wise noise injection (LNI) regularization to improve the model's robustness and generalization.
• Score: 7.207685588038045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sequential recommendation models have achieved state-of-the-art performance using self-attention mechanism. It has since been found that moving beyond only using item ID and positional embeddings leads to a significant accuracy boost when predicting the next item. In recent literature, it was reported that a multi-dimensional kernel embedding with temporal contextual kernels to capture users' diverse behavioral patterns results in a substantial performance improvement. In this study, we further improve the sequential recommender model's robustness and generalization by introducing a mix-attention mechanism with a layer-wise noise injection (LNI) regularization. We refer to our proposed model as adaptive robust sequential recommendation framework (ADRRec), and demonstrate through extensive experiments that our model outperforms existing self-attention architectures.

Related papers

• 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)
• Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendation [47.29682938439268]
  We propose a novel Counterfactual Fine-Tuning (CFT) method to improve user preference modeling. We employ counterfactual reasoning to identify the causal effects of behavior sequences on model output. Experiments on real-world datasets demonstrate that CFT effectively improves behavior sequence modeling.
  arXiv  Detail & Related papers  (2024-10-30T08:41:13Z)
• 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)
• Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models [49.458914600467324]
  We introduce the Schr"odinger Bridge into diffusion-based sequential recommendation models, creating the SdifRec model. We also propose an extended version of SdifRec called con-SdifRec, which utilizes user clustering information as a guiding condition.
  arXiv  Detail & Related papers  (2024-08-30T09:10:38Z)
• Behavior-Dependent Linear Recurrent Units for Efficient Sequential Recommendation [18.75561256311228]
  RecBLR is an Efficient Sequential Recommendation Model based on Behavior-Dependent Linear Recurrent Units. Our model significantly enhances user behavior modeling and recommendation performance.
  arXiv  Detail & Related papers  (2024-06-18T13:06:58Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• 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)
• 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)
• 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.