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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 241–250 of 554 papers

TitleStatusHype
Image Fusion for Cross-Domain Sequential Recommendation—0
Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation—0
Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models—0
Lightweight yet Fine-grained: A Graph Capsule Convolutional Network with Subspace Alignment for Shared-account Sequential RecommendationCode0
LLM is Knowledge Graph Reasoner: LLM's Intuition-aware Knowledge Graph Reasoning for Cold-start Sequential Recommendation—0
A Survey on Sequential Recommendation—0
Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRecCode0
Multimodal Difference Learning for Sequential Recommendation—0
Preference Discerning with LLM-Enhanced Generative Retrieval—0
PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information—0
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