A Review on Multiple Data Source Based Recommendation Systems

Recommender systems are used by the content or item providers. The content could be video, music, etc. For example, Netflix recommends movies or TV shows, Amazon recommends books or other items, etc. Most content providers use their platform to collect data from their users and then use the collected data to design a recommender system. However, the recommendation results are more useful if a recommender system uses multiple data sources. Both Matrix Factorization (MF) and Deep Neural Network (DNN) models are used to design multiple data source-based recommenders. This paper reviews various approaches that use multiple data sources to design recommender systems using MF and DNN models.