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Sequential Recommendation
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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.
논문
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Unconditional Diffusion for Generative Sequential Recommendation
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation
A Systematic Replicability and Comparative Study of BSARec and SASRec for Sequential Recommendation
C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized Recommendation
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
RecGPT: A Foundation Model for Sequential Recommendation
GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation
Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach
Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain Recommendation
Flow Matching based Sequential Recommender Model
LARES: Latent Reasoning for Sequential Recommendation
DIFF: Dual Side-Information Filtering and Fusion for Sequential Recommendation
HMamba: Hyperbolic Mamba for Sequential Recommendation
M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation
A Framework for Elastic Adaptation of User Multiple Intents in Sequential Recommendation
X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation
Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair Selection
AlphaFuse: Learn ID Embeddings for Sequential Recommendation in Null Space of Language Embeddings
Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation
Intent-aware Diffusion with Contrastive Learning for Sequential Recommendation
Hierarchical Attention Fusion of Visual and Textual Representations for Cross-Domain Sequential Recommendation
Improving Sequential Recommenders through Counterfactual Augmentation of System Exposure
Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation
Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential Recommendation
Revisiting Self-Attentive Sequential Recommendation
Slow Thinking for Sequential Recommendation
A Novel Mamba-based Sequential Recommendation Method
JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation
BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation
AdaptRec: A Self-Adaptive Framework for Sequential Recommendations with Large Language Models
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