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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 231–240 of 554 papers

TitleStatusHype
SS4Rec: Continuous-Time Sequential Recommendation with State Space ModelsCode0
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential RecommendationCode0
A Zero-Shot Generalization Framework for LLM-Driven Cross-Domain Sequential Recommendation—0
Improving Minimax Group Fairness in Sequential RecommendationCode0
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation—0
Multi-Grained Patch Training for Efficient LLM-based RecommendationCode0
ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential RecommendationCode0
Style4Rec: Enhancing Transformer-based E-commerce Recommendation Systems with Style and Shopping Cart Information—0
Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation—0
An Efficient Attention Mechanism for Sequential Recommendation Tasks: HydraRec—0
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