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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 1120 of 554 papers

TitleStatusHype
Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain RecommendationCode0
LARES: Latent Reasoning for Sequential Recommendation0
Flow Matching based Sequential Recommender ModelCode1
DIFF: Dual Side-Information Filtering and Fusion for Sequential RecommendationCode1
HMamba: Hyperbolic Mamba for Sequential Recommendation0
M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation0
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation0
A Framework for Elastic Adaptation of User Multiple Intents in Sequential Recommendation0
X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation0
Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair SelectionCode1
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