Hybrid recommendation system combining collaborative filtering and content-based recommendation with keyword extraction

Abstract

With the development of recommendation systems, large amount of information collected from e-commerce could help customers to find the potential interesting products. Collaborative filtering and content-based recommendation systems are two common recommendation systems. While collaborative filtering has the problem of cold-start, content-based recommendation system could not explore the potential interests of users. Hybrid system combining these two techniques could achieve better results. This paper applies hybrid recommendation methods to the Amazon food reviews and evaluate the results in the aspects of precision, recall, diversity and novelty. It is found that the weighted hybrid recommendation system combing 0.95 weight of collaborative filtering and 0.05 content-based recommendation system achieves a good precision and diversity.