Research on Collaborative Filtering Recommendation Algorithm Based on Mahout

Recommendation system has been applied in many fields, such as Human Computer Interaction, Information Retrieval, and so on. Essentially speaking, these are based on data mining. Among the many algorithms used in the recommendation system, collaborative filtering algorithm is one of the most mature and most widely used algorithm. With the coming of big data era, a variety of recommendation algorithm surge, and distributed recommendation algorithm has become a hot research topic. As a sub project of Hadoop, Mahout is also very obvious to support collaborative filtering algorithm. In order to deeply analyze the operation principle and the use effect of Collaborative Filtering Recommendation Algorithm in mahout, we designed a combination model composed of the recommender and the similarity measure. Using the custom data set, we write some Java classes to test collaborative filtering algorithm based on user and the collaborative filtering algorithm based on the item, the experimental results shows that our combination model get a better classification effect