Collaborative Filtering with Label Consistent Restricted Boltzmann Machine
원문 초록
The possibility of employing restricted Boltzmann machine (RBM) for collaborative filtering has been known for about a decade. However, there has been hardly any work on this topic since 2007. This work revisits the application of RBM in recommender systems. RBM based collaborative filtering only used the rating information; this is an unsupervised architecture. This work adds supervision by exploiting user demographic information and item metadata. A network is learned from the representation layer to the labels (metadata). The proposed label consistent RBM formulation improves significantly on the existing RBM based approach and yield results at par with the state-of-the-art latent factor based models.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering
Uncertainty in Repeated Implicit Feedback as a Measure of Reliability
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.