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Sequential Recommendation

Sequential recommendation is a sophisticated approach to providing personalized suggestions by analyzing users' historical interactions in a sequential manner. Unlike traditional recommendation systems, which consider items in isolation, sequential recommendation takes into account the temporal order of user actions. This method is particularly valuable in domains where the sequence of events matters, such as streaming services, e-commerce platforms, and social media.

Papers

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Showing 501–525 of 554 papers

TitleStatusHype
Towards Neural Mixture Recommender for Long Range Dependent User Sequences—0
Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention—0
Intent-Enhanced Data Augmentation for Sequential Recommendation—0
Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation—0
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation—0
Invariant representation learning for sequential recommendation—0
Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings—0
Item Association Factorization Mixed Markov Chains for Sequential Recommendation—0
JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture—0
LARES: Latent Reasoning for Sequential Recommendation—0
Sequential Recommendation with Causal Behavior Discovery—0
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems—0
Laser: Parameter-Efficient LLM Bi-Tuning for Sequential Recommendation with Collaborative Information—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation—0
Sequential Recommendation with Diffusion Models—0
Learning Graph ODE for Continuous-Time Sequential Recommendation—0
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation—0
Learning Post-Hoc Causal Explanations for Recommendation—0
Gumble Softmax For User Behavior Modeling—0
Learning to Augment for Casual User Recommendation—0
Learning to Learn a Cold-start Sequential Recommender—0
Learning to Structure Long-term Dependence for Sequential Recommendation—0
Unifying Generative and Dense Retrieval for Sequential Recommendation—0
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation—0
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