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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 161170 of 554 papers

TitleStatusHype
Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential RecommendationCode1
SEMINAR: Search Enhanced Multi-modal Interest Network and Approximate Retrieval for Lifelong Sequential Recommendation0
Preference Distillation for Personalized Generative RecommendationCode0
Learning Positional Attention for Sequential RecommendationCode0
UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential RecommendationsCode1
EAGER: Two-Stream Generative Recommender with Behavior-Semantic CollaborationCode1
Behavior-Dependent Linear Recurrent Units for Efficient Sequential RecommendationCode0
DELRec: Distilling Sequential Pattern to Enhance LLMs-based Sequential RecommendationCode0
DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation0
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender SystemCode0
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