32 citations · 124 across the 23 of their papers we have counts for
7 papers · 1 filter
PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data
Hao Wang
Matrix Factorization is one of the most successful recommender system techniques over the past decade. However, the classic probabilistic theory framework for matrix factorization…
Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems
Hao Wang
Learning to rank is an effective recommendation approach since its introduction around 2010. Famous algorithms such as Bayesian Personalized Ranking and Collaborative Less is More…
DotMat: Solving Cold-start Problem and Alleviating Sparsity Problem for Recommender Systems
Hao Wang
Cold-start and sparsity problem are two key intrinsic problems to recommender systems. During the past two decades, researchers and industrial practitioners have spent considerable…
Theoretically Accurate Regularization Technique for Matrix Factorization based Recommender Systems
Hao Wang
Regularization is a popular technique to solve the overfitting problem of machine learning algorithms. Most regularization technique relies on parameter selection of the regulariza…
KL-Mat : Fair Recommender System via Information Geometry
Hao Wang
Recommender system has intrinsic problems such as sparsity and fairness. Although it has been widely adopted for the past decades, research on fairness of recommendation algorithms…
RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement
Hao Wang
Matrix Factorization is a widely adopted technique in the field of recommender system. Matrix Factorization techniques range from SVD, LDA, pLSA, SVD++, MatRec, Zipf Matrix Factori…
MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting
Hao Wang
Movie Recommender System is widely applied in commercial environments such as NetFlix and Tubi. Classic recommender models utilize technologies such as collaborative filtering, lea…