Implementation and Effectiveness Evaluation of Four Common Algorithms of Recommendation Systems - User Collaboration Filter, Item-based Collaborative Filtering, Matrix Factorization and Neural Collaborative Filtering

Abstract

Recommendation systems are widely used in various industries and are seen as one of the effective methods in reducing information overload. This paper selected four common algorithms for implementation and evaluation among many recommendation algorithms. Two traditional collaborative filtering algorithms - User Collaboration Filter and Item-based Collaborative Filtering; most popular algorithms in the recommendation field - Matrix Factorization; and Neural Collaborative Filtering - the algorithm based on Matrix Factorization and combined with neural networks.