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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

Showing 7180 of 554 papers

TitleStatusHype
Lightweight yet Fine-grained: A Graph Capsule Convolutional Network with Subspace Alignment for Shared-account Sequential RecommendationCode0
A Survey on Sequential Recommendation0
LLM is Knowledge Graph Reasoner: LLM's Intuition-aware Knowledge Graph Reasoning for Cold-start Sequential Recommendation0
Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRecCode0
Multimodal Difference Learning for Sequential Recommendation0
Augmenting Sequential Recommendation with Balanced Relevance and DiversityCode1
Preference Discerning with LLM-Enhanced Generative Retrieval0
Scaling Sequential Recommendation Models with TransformersCode1
Temporal Linear Item-Item Model for Sequential RecommendationCode1
PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information0
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