arXiv:2205.12371
공식 코드 · Papers with Code
recommenderlab: An R Framework for Developing and Testing Recommendation Algorithms
원문 초록
Algorithms that create recommendations based on observed data have significant commercial value for online retailers and many other industries. Recommender systems have a significant research community, and studying such systems is part of most modern data science curricula. While there is an abundance of software that implements recommendation algorithms, there is little in terms of supporting recommender system research and education. This paper describes the open-source software recommenderlab which was created with supporting research and education in mind. The package can be directly installed in R or downloaded from https://github.com/mhahsler/recommenderlab.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
Boosting Parameter Efficiency in LLM-Based Recommendation through Sophisticated Pruning
Kamae: Bridging Spark and Keras for Seamless ML Preprocessing
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent
토론
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