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Found 7 papers, 6 papers with code
Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation
In this paper, Intent contrastive learning with Cross Subsequences for sequential Recommendation (ICSRec) is proposed to model users' latent intentions.
Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation
Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data.
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation
Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks.
Sequential Recommendation with Probabilistic Logical Reasoning
To better capture the uncertainty and evolution of user tastes, SR-PLR embeds users and items with a probabilistic method and conducts probabilistic logical reasoning on users' interaction patterns.
Frequency Enhanced Hybrid Attention Network for Sequential Recommendation
However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information.
Meta-optimized Contrastive Learning for Sequential Recommendation
By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation.
Sequential Recommendation with Diffusion Models
Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN), have been successfully applied in sequential recommendation.
