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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 121–130 of 554 papers

TitleStatusHype
TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality ModelCode1
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation—0
Data Augmentation for Sequential Recommendation: A SurveyCode3
HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User ModelingCode4
Measuring Recency Bias In Sequential Recommendation Systems—0
Multi-intent Aware Contrastive Learning for Sequential Recommendation—0
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive LearningCode0
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential RecommendationCode0
Generative Recommender with End-to-End Learnable Item TokenizationCode1
Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings—0
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