Papers with Code 아카이브
Sequential Recommendation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 13
- 보존된 평가 기록
- 20
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
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec
Multimodal Difference Learning for Sequential Recommendation
Augmenting Sequential Recommendation with Balanced Relevance and Diversity
Preference Discerning with LLM-Enhanced Generative Retrieval
Temporal Linear Item-Item Model for Sequential Recommendation
Scaling Sequential Recommendation Models with Transformers
PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information
ULMRec: User-centric Large Language Model for Sequential Recommendation
Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models
Precision Profile Pollution Attack on Sequential Recommenders via Influence Function
Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation
Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy
Break the ID-Language Barrier: An Adaption Framework for Sequential Recommendation
Unifying Generative and Dense Retrieval for Sequential Recommendation
GOT4Rec: Graph of Thoughts for Sequential Recommendation
Data Watermarking for Sequential Recommender Systems
Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential Recommendation
Few-shot Model Extraction Attacks against Sequential Recommender Systems
Item Association Factorization Mixed Markov Chains for Sequential Recommendation
LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendation
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation
Transferable Sequential Recommendation via Vector Quantized Meta Learning
Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation
LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems
DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation
Dual Conditional Diffusion Models for Sequential Recommendation
Modeling Temporal Positive and Negative Excitation for Sequential Recommendation
SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item Similarity
Pay Attention to Attention for Sequential Recommendation
Generative Diffusion Models for Sequential Recommendations
Preference Diffusion for Recommendation
Generate and Instantiate What You Prefer: Text-Guided Diffusion for Sequential Recommendation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.