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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 281–290 of 554 papers

TitleStatusHype
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation—0
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
Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings—0
Enhancing Sequential Music Recommendation with Personalized Popularity AwarenessCode0
Laser: Parameter-Efficient LLM Bi-Tuning for Sequential Recommendation with Collaborative Information—0
Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models—0
Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item PredictionCode0
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