When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendation

• URL: http://arxiv.org/abs/2409.12730v1
• Date: Thu, 19 Sep 2024 12:55:34 GMT
• Title: When SparseMoE Meets Noisy Interactions: An Ensemble View on Denoising Recommendation
• Authors: Weipu Chen, Zhuangzhuang He, Fei Liu,
• Abstract summary: We propose a novel Adaptive Ensemble Learning (AEL) for denoising recommendation. AEL employs a sparse gating network as a brain, selecting suitable experts to synthesize appropriate denoising capacities. To address the ensemble learning shortcoming of model complexity, we also proposed a novel method that stacks components to create sub-recommenders.
• Score: 3.050721435894337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning user preferences from implicit feedback is one of the core challenges in recommendation. The difficulty lies in the potential noise within implicit feedback. Therefore, various denoising recommendation methods have been proposed recently. However, most of them overly rely on the hyperparameter configurations, inevitably leading to inadequacies in model adaptability and generalization performance. In this study, we propose a novel Adaptive Ensemble Learning (AEL) for denoising recommendation, which employs a sparse gating network as a brain, selecting suitable experts to synthesize appropriate denoising capacities for different data samples. To address the ensemble learning shortcoming of model complexity and ensure sub-recommender diversity, we also proposed a novel method that stacks components to create sub-recommenders instead of directly constructing them. Extensive experiments across various datasets demonstrate that AEL outperforms others in kinds of popular metrics, even in the presence of substantial and dynamic noise. Our code is available at https://github.com/cpu9xx/AEL.

Related papers

• Large Language Model Enhanced Hard Sample Identification for Denoising Recommendation [4.297249011611168]
  Implicit feedback is often used to build recommender systems. Previous studies have attempted to alleviate this by identifying noisy samples based on their diverged patterns. We propose a Large Language Model Enhanced Hard Sample Denoising framework.
  arXiv  Detail & Related papers  (2024-09-16T14:57:09Z)
• Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation [69.60321475454843]
  We propose DPCPL, the first pre-training and prompt-tuning paradigm tailored for Multi-Behavior Sequential Recommendation. In the pre-training stage, we propose a novel Efficient Behavior Miner (EBM) to filter out the noise at multiple time scales. Subsequently, we propose to tune the pre-trained model in a highly efficient manner with the proposed Customized Prompt Learning (CPL) module.
  arXiv  Detail & Related papers  (2024-08-21T06:48:38Z)
• Robust Learning under Hybrid Noise [24.36707245704713]
  We propose a novel unified learning framework called "Feature and Label Recovery" (FLR) to combat the hybrid noise from the perspective of data recovery.
  arXiv  Detail & Related papers  (2024-07-04T16:13:25Z)
• TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content [21.90660366765994]
  We propose a trustworthy sequential recommendation method via noisy user-generated multi-modal content. Specifically, we capture the consistency and complementarity of user-generated multi-modal content to mitigate noise interference. In addition, we design a trustworthy decision mechanism that integrates subjective user perspective and objective item perspective.
  arXiv  Detail & Related papers  (2024-04-26T08:23:36Z)
• Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential Recommendation [13.355017204983973]
  Sequential recommender systems (SRSs) aim to suggest next item for a user based on her historical interaction sequences. We propose a novel model named Multi-level Sequence Denoising with Cross-signal Contrastive Learning (MSDCCL) for sequential recommendation.
  arXiv  Detail & Related papers  (2024-04-22T04:57:33Z)
• ROPO: Robust Preference Optimization for Large Language Models [59.10763211091664]
  We propose an iterative alignment approach that integrates noise-tolerance and filtering of noisy samples without the aid of external models. Experiments on three widely-used datasets with Mistral-7B and Llama-2-7B demonstrate that ROPO significantly outperforms existing preference alignment methods.
  arXiv  Detail & Related papers  (2024-04-05T13:58:51Z)
• An Adaptive Cost-Sensitive Learning and Recursive Denoising Framework for Imbalanced SVM Classification [12.986535715303331]
  Category imbalance is one of the most popular and important issues in the domain of classification. Emotion classification model trained on imbalanced datasets easily leads to unreliable prediction.
  arXiv  Detail & Related papers  (2024-03-13T09:43:14Z)
• DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification [55.306583814017046]
  We present a novel difficulty-aware semantic augmentation (DASA) approach for speaker verification. DASA generates diversified training samples in speaker embedding space with negligible extra computing cost. The best result achieves a 14.6% relative reduction in EER metric on CN-Celeb evaluation set.
  arXiv  Detail & Related papers  (2023-10-18T17:07:05Z)
• DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions [52.63323657077447]
  We propose DNMOT, an end-to-end trainable DeNoising Transformer for multiple object tracking. Specifically, we augment the trajectory with noises during training and make our model learn the denoising process in an encoder-decoder architecture. We conduct extensive experiments on the MOT17, MOT20, and DanceTrack datasets, and the experimental results show that our method outperforms previous state-of-the-art methods by a clear margin.
  arXiv  Detail & Related papers  (2023-09-09T04:40:01Z)
• 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)
• Probabilistic and Variational Recommendation Denoising [56.879165033014026]
  Learning from implicit feedback is one of the most common cases in the application of recommender systems. We propose probabilistic and variational recommendation denoising for implicit feedback. We employ the proposed DPI and DVAE on four state-of-the-art recommendation models and conduct experiments on three datasets.
  arXiv  Detail & Related papers  (2021-05-20T08:59:44Z)

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.