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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 341–350 of 554 papers

TitleStatusHype
Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling—0
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential RecommendationCode0
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai—0
The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential RecommendationCode0
Empowering Sequential Recommendation from Collaborative Signals and Semantic RelatednessCode0
Multi-Tower Multi-Interest Recommendation with User Representation Repel—0
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement LearningCode0
Can Small Language Models be Good Reasoners for Sequential Recommendation?—0
BiVRec: Bidirectional View-based Multimodal Sequential Recommendation—0
BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation—0
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