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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 8190 of 554 papers

TitleStatusHype
ULMRec: User-centric Large Language Model for Sequential Recommendation0
Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language ModelsCode1
Precision Profile Pollution Attack on Sequential Recommenders via Influence Function0
Oracle-guided Dynamic User Preference Modeling for Sequential RecommendationCode1
Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy0
Unifying Generative and Dense Retrieval for Sequential Recommendation0
Break the ID-Language Barrier: An Adaption Framework for Sequential Recommendation0
GOT4Rec: Graph of Thoughts for Sequential Recommendation0
Data Watermarking for Sequential Recommender SystemsCode0
Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential RecommendationCode0
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