32 citations · 124 across the 23 of their papers we have counts for
23 papers
GreyShot: Zeroshot and Privacy-preserving Recommender System by GM(1,1) Model
Hao Wang
Every recommendation engineer needs to face the cold start problem when building his system. During the past decades, most scientists adopted transfer learning and meta learning to…
TriMat: Context-aware Recommendation by Tri-Matrix Factorization
Hao Wang
Search engine is the symbolic technology of Web 2.0, and many people used to believe recommender systems is the new frontier of Web 3.0. In the past 10 years, with the advent of Ti…
Emotion-based Recommender System
Hao Wang
Recommender system is one of the most critical technologies for large internet companies such as Amazon and TikTok. Although millions of users use recommender systems globally ever…
A Click-Through Rate Prediction Method Based on Cross-Importance of Multi-Order Features
Hao Wang, Nao Li
Most current click-through rate prediction(CTR)models create explicit or implicit high-order feature crosses through Hadamard product or inner product, with little attention to the…
Mitigating Position Bias with Regularization for Recommender Systems
Hao Wang
Fairness is a popular research topic in recent years. A research topic closely related to fairness is bias and debiasing. Among different types of bias problems, position bias is o…
Enhancing Recommender System Performance by Histogram Equalization
Hao Wang
Recommender system has been researched for decades with millions of different versions of algorithms created in the industry. In spite of the huge amount of work spent on the field…
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models
Hao Wang
Recommender system is adored in the internet industry as one of the most profitable technologies. Unlike other sectors such as fraud detection in the Fintech industry, recommender…
Analysis and Visualization of the Parameter Space of Matrix Factorization-based Recommender Systems
Hao Wang
Recommender system is the most successful commercial technology in the past decade. Technical mammoth such as Temu, TikTok and Amazon utilize the technology to generate enormous re…
PowerMat: context-aware recommender system without user item rating values that solves the cold-start problem
Hao Wang
Recommender systems serves as an important technical asset in many modern companies. With the increasing demand for higher precision of the technology, more and more research and i…
Kernel-CF: Collaborative filtering done right with social network analysis and kernel smoothing
Hao Wang
Collaborative filtering is the simplest but oldest machine learning algorithm in the field of recommender systems. In spite of its long history, it remains a discussion topic in re…
Fair Recommendation by Geometric Interpretation and Analysis of Matrix Factorization
Hao Wang
Matrix factorization-based recommender system is in effect an angle preserving dimensionality reduction technique. Since the frequency of items follows power-law distribution, most…
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…
MatMat: Matrix Factorization by Matrix Fitting
Hao Wang
Matrix factorization is a widely adopted recommender system technique that fits scalar rating values by dot products of user feature vectors and item feature vectors. However, the…
ZeroMat: Solving Cold-start Problem of Recommender System with No Input Data
Hao Wang
Recommender system is an applicable technique in most E-commerce commercial product technical designs. However, nearly all recommender system faces a challenge called the cold-star…
Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction
Hao Wang
Recommender system recommends interesting items to users based on users' past information history. Researchers have been paying attention to improvement of algorithmic performance…
MatRec: Matrix Factorization for Highly Skewed Dataset
Hao Wang, Bing Ruan
Recommender systems is one of the most successful AI technologies applied in the internet cooperations. Popular internet products such as TikTok, Amazon, and YouTube have all integ…
Quantitative analysis of Matthew effect and sparsity problem of recommender systems
Hao Wang, Zonghu Wang, Weishi Zhang
Recommender systems have received great commercial success. Recommendation has been used widely in areas such as e-commerce, online music FM, online news portal, etc. However, seve…