https://github.com/zzxslp/CosRec
공식 코드CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models
Generative Recommender with End-to-End Learnable Item Tokenization
Memory Augmented Graph Neural Networks for Sequential Recommendation
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Context-Aware Sequential Model for Multi-Behaviour Recommendation
Text Is All You Need: Learning Language Representations for Sequential Recommendation
Zero-Shot Next-Item Recommendation using Large Pretrained Language Models
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention
gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling
A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
Dynamic Memory based Attention Network for Sequential Recommendation
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
HAM: Hybrid Associations Models for Sequential Recommendation
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning
MACRec: a Multi-Agent Collaboration Framework for Recommendation
TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.