S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

• URL: http://arxiv.org/abs/2008.07873v1
• Date: Tue, 18 Aug 2020 11:44:10 GMT
• Title: S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
• Authors: Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang and Ji-Rong Wen
• Abstract summary: 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.
• Score: 104.87483578308526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained with this loss is prone to suffer from data sparsity problem. Since it overemphasizes the final performance, the association or fusion between context data and sequence data has not been well captured and utilized for sequential recommendation. To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture. The main idea of our approach is to utilize the intrinsic data correlation to derive self-supervision signals and enhance the data representations via pre-training methods for improving sequential recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence by utilizing the mutual information maximization (MIM) principle. MIM provides a unified way to characterize the correlation between different types of data, which is particularly suitable in our scenario. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods, especially when only limited training data is available. Besides, we extend our self-supervised learning method to other recommendation models, which also improve their performance.

Related papers

• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• Semantic-Enhanced Relational Metric Learning for Recommender Systems [27.330164862413184]
  Recently, metric learning methods have been received great attention in recommendation community, which is inspired by the translation mechanism in knowledge graph. We propose a joint Semantic-Enhanced Metric Learning framework to tackle the problem in recommender systems. Specifically the semantic signal is first extracted from the target reviews containing abundant features and personalized user preferences. A novel regression model is then designed via leveraging the extracted semantic signal to improve the discriminative ability of original relation-based training process.
  arXiv  Detail & Related papers  (2024-06-07T11:54:50Z)
• Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation [36.81597777126902]
  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.
  arXiv  Detail & Related papers  (2022-04-21T14:30:56Z)
• 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)
• WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models [24.455665093145818]
  We propose a novel model-agnostic training approach called WSLRec, which adopts a three-stage framework: pre-training, top-$k$ mining, intrinsic and fine-tuning. WSLRec resolves the incompleteness problem by pre-training models on extra weak supervisions from model-free methods like BR and ItemCF, while resolving the inaccuracy problem by leveraging the top-$k$ mining to screen out reliable user-item relevance from weak supervisions for fine-tuning.
  arXiv  Detail & Related papers  (2022-02-28T08:55:12Z)
• Top-N Recommendation with Counterfactual User Preference Simulation [26.597102553608348]
  Top-N recommendation, which aims to learn user ranking-based preference, has long been a fundamental problem in a wide range of applications. In this paper, we propose to reformulate the recommendation task within the causal inference framework to handle the data scarce problem.
  arXiv  Detail & Related papers  (2021-09-02T14:28:46Z)
• 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)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Relation-Guided Representation Learning [53.60351496449232]
  We propose a new representation learning method that explicitly models and leverages sample relations. Our framework well preserves the relations between samples. By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
  arXiv  Detail & Related papers  (2020-07-11T10:57:45Z)

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.