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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 211220 of 554 papers

TitleStatusHype
Harnessing Large Language Models for Text-Rich Sequential RecommendationCode1
A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment RecommendationCode1
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai0
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential RecommendationCode1
The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential RecommendationCode0
Empowering Sequential Recommendation from Collaborative Signals and Semantic RelatednessCode0
Repeated Padding for Sequential RecommendationCode1
Multi-Tower Multi-Interest Recommendation with User Representation Repel0
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement LearningCode0
Can Small Language Models be Good Reasoners for Sequential Recommendation?0
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