activity
20192025
most citedQuantitative analysis of Matthew effect and sparsity problem of recommender systems

32 citations · 77 across the 12 of their papers we have counts for

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12 papers · 1 filter

cs.IR2025

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…

cs.IR2025

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…

cs.IR2025

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…

cs.IR2024

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…

cs.IR2023

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…

cs.IR2023

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…