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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 426–450 of 554 papers

TitleStatusHype
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation—0
Rethinking Large Language Model Architectures for Sequential Recommendations—0
Dual Conditional Diffusion Models for Sequential Recommendation—0
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation—0
Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention—0
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation—0
Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models—0
Direct Preference Optimization for LLM-Enhanced Recommendation Systems—0
Route Optimization via Environment-Aware Deep Network and Reinforcement Learning—0
Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation—0
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation—0
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation—0
Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of Items—0
ELASTIC: Efficient Linear Attention for Sequential Interest Compression—0
Sample Enrichment via Temporary Operations on Subsequences for Sequential Recommendation—0
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation—0
SANST: A Self-Attentive Network for Next Point-of-Interest Recommendation—0
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework—0
Designing a Sequential Recommendation System for Heterogeneous Interactions Using Transformers—0
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation—0
Towards commands recommender system in BIM authoring tool using transformers—0
Scaling Law of Large Sequential Recommendation Models—0
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation—0
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search—0
Extracting Attentive Social Temporal Excitation for Sequential Recommendation—0
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