Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation

• URL: http://arxiv.org/abs/2204.10128v1
• Date: Thu, 21 Apr 2022 14:30:56 GMT
• Title: Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation
• Authors: Yongjing Hao, Pengpeng Zhao, Xuefeng Xian, Guanfeng Liu, Deqing Wang, Lei Zhao, Yanchi Liu and Victor S. Sheng
• Abstract summary: We propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec) LMA4Rec first takes model augmentation as a supplementary method for data augmentation to generate views. Next, self-supervised learning is used between the contrastive views to extract self-supervised signals from an original sequence.
• Score: 36.81597777126902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential Recommendation aims to predict the next item based on user behaviour. Recently, Self-Supervised Learning (SSL) has been proposed to improve recommendation performance. However, most of existing SSL methods use a uniform data augmentation scheme, which loses the sequence correlation of an original sequence. To this end, in this paper, we propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec). Specifically, LMA4Rec first takes model augmentation as a supplementary method for data augmentation to generate views. Then, LMA4Rec uses learnable Bernoulli dropout to implement model augmentation learnable operations. Next, self-supervised learning is used between the contrastive views to extract self-supervised signals from an original sequence. Finally, experiments on three public datasets show that the LMA4Rec method effectively improves sequential recommendation performance compared with baseline methods.

Related papers

• Diffusion-based Contrastive Learning for Sequential Recommendation [6.3482831836623355]
  We propose a Context-aware Diffusion-based Contrastive Learning for Sequential Recommendation, named CaDiRec. CaDiRec employs a context-aware diffusion model to generate alternative items for the given positions within a sequence. 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)
• Towards Universal Sequence Representation Learning for Recommender Systems [98.02154164251846]
  We present a novel universal sequence representation learning approach, named UniSRec. The proposed approach utilizes the associated description text of items to learn transferable representations across different recommendation scenarios. Our approach can be effectively transferred to new recommendation domains or platforms in a parameter-efficient way.
  arXiv  Detail & Related papers  (2022-06-13T07:21:56Z)
• Improving Contrastive Learning with Model Augmentation [123.05700988581806]
  The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity and noise issues in sequences, a new self-supervised learning (SSL) paradigm is proposed to improve the performance.
  arXiv  Detail & Related papers  (2022-03-25T06:12:58Z)
• 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)
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [104.87483578308526]
  We propose the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-18T11:44:10Z)
• Sequential Recommendation with Self-Attentive Multi-Adversarial Network [101.25533520688654]
  We present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time.
  arXiv  Detail & Related papers  (2020-05-21T12:28:59Z)

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.