Slope One Predictors for Online Rating-Based Collaborative Filtering
원문 초록
Rating-based collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. We propose three related slope one schemes with predictors of the form f(x) = x + b, which precompute the average difference between the ratings of one item and another for users who rated both. Slope one algorithms are easy to implement, efficient to query, reasonably accurate, and they support both online queries and dynamic updates, which makes them good candidates for real-world systems. The basic slope one scheme is suggested as a new reference scheme for collaborative filtering. By factoring in items that a user liked separately from items that a user disliked, we achieve results competitive with slower memory-based schemes over the standard benchmark EachMovie and Movielens data sets while better fulfilling the desiderata of CF applications.
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.