A Comparative Analysis of Memory-Based and Model-Based Collaborative Filtering on Recommender System Implementation

Today, several successful companies like Uber, Airbnb, and others have adopted sharing economy business models. The increasing growth of websites and applications adopting this model pushes companies to develop differentiation strategies. One of the strategies is to use emerging technologies to offer a better customer experience. Recommender systems (RSs) are AI-based solutions that can provide customized recommendations. To implement an RS in a sharing economy platform, this study intends to compare the performance of two recommendation-system approaches based on their accuracy, computation time, and scalability. The Netflix dataset was used to compare matrix factorization and memory-based techniques based on their performances using offline testing. The results of the study indicate that memory-based methods are more accurate for small datasets but have computation time limitations for large datasets. Single-value decomposition methods scale better than memory-based algorithms.