arXiv:1706.04453
공식 코드 · Papers with Code
Hybrid Collaborative Recommendation via Semi-AutoEncoder
원문 초록
In this paper, we present a novel structure, Semi-AutoEncoder, based on AutoEncoder. We generalize it into a hybrid collaborative filtering model for rating prediction as well as personalized top-n recommendations. Experimental results on two real-world datasets demonstrate its state-of-the-art performances.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
Dual-View Disentangled Multi-Intent Learning for Enhanced Collaborative Filtering
Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering
토론
이 논문과 연결된 포럼 글이 아직 없습니다.