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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 21–30 of 554 papers

TitleStatusHype
Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair SelectionCode1
Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation—0
Intent-aware Diffusion with Contrastive Learning for Sequential RecommendationCode1
Hierarchical Attention Fusion of Visual and Textual Representations for Cross-Domain Sequential Recommendation—0
Adaptive Long-term Embedding with Denoising and Augmentation for RecommendationCode0
Improving Sequential Recommenders through Counterfactual Augmentation of System ExposureCode0
Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation—0
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential RecommendationCode0
Slow Thinking for Sequential Recommendation—0
Revisiting Self-Attentive Sequential RecommendationCode4
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