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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 251260 of 554 papers

TitleStatusHype
LLaRA: Large Language-Recommendation AssistantCode1
E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential RecommendationCode1
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential RecommendationCode0
GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training0
Scaling Law of Large Sequential Recommendation Models0
Collaborative Word-based Pre-trained Item Representation for Transferable RecommendationCode1
Mixed Attention Network for Cross-domain Sequential RecommendationCode1
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation0
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising ApproachCode0
Rethinking Cross-Domain Sequential Recommendation under Open-World AssumptionsCode1
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