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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

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Showing 31–40 of 554 papers

TitleStatusHype
JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture—0
A Novel Mamba-based Sequential Recommendation Method—0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation—0
BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation—0
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential RecommendationCode1
AdaptRec: A Self-Adaptive Framework for Sequential Recommendations with Large Language Models—0
Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation—0
Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement LearningCode2
Filtering with Time-frequency Analysis: An Adaptive and Lightweight Model for Sequential Recommender Systems Based on Discrete Wavelet Transform—0
Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation—0
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