80 citations · 178 across the 56 of their papers we have counts for
7 papers · 1 filter
Evolution of the Online Rating Platform Data Structures and its Implications for Recommender Systems
Hao Wang
Online rating platform represents the new trend of online cultural and commercial goods consumption. The user rating data on such platforms are foods for recommender system algorit…
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…
Effective Visualization and Analysis of Recommender Systems
Hao Wang
Recommender system exists everywhere in the business world. From Goodreads to TikTok, customers of internet products become more addicted to the products thanks to the technology.…
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation
Yongqiang Han, Likang Wu, Hao Wang +5
Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item. Although lots of r…