Quantitative analysis of Matthew effect and sparsity problem of recommender systems

Abstract

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, several problems related to input data structure pose serious challenge to recommender system performance. Two of these problems are Matthew effect and sparsity problem. Matthew effect heavily skews recommender system output towards popular items. Data sparsity problem directly affects the coverage of recommendation result. Collaborative filtering is a simple benchmark ubiquitously adopted in the industry as the baseline for recommender system design. Understanding the underlying mechanism of collaborative filtering is crucial for further optimization. In this paper, we do a thorough quantitative analysis on Matthew effect and sparsity problem in the particular context setting of collaborative filtering. We compare the underlying mechanism of user-based and item-based collaborative filtering and give insight to industrial recommender system builders.

References in corpus (1)

• Ask the GRU: Multi-Task Learning for Deep Text Recommendations

Cited by in corpus (10)

• A Survey on Popularity Bias in Recommender Systems
• Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
• Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder
• ZeroMat: Solving Cold-start Problem of Recommender System with No Input Data
• MatMat: Matrix Factorization by Matrix Fitting
• Medusa: Blockchain Powered Log Storage System
• Is Human Culture Locked by Evolution?
• Effective Visualization and Analysis of Recommender Systems
• Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction
• LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models