Improving item-based recommendation accuracy with user's preferences on Apache Mahout

Recommendation systems play a critical role in the Information Science application domain, especially in ecommerce ecosystems. In almost all recommender systems, statistical methods and machine learning techniques are used to recommend items to the users. Although the user-based collaborative filtering approaches have been applied successfully in many different domains, some serious challenges remain especially in regards to large e-commerce sites, for recommender systems need to manage millions of users and millions of catalog products. In particular, the need to scan a vast number of potential neighbors makes it very hard to compute predictions. Many researchers have been trying to come up with solutions like using neighborhood-based collaborative filtering algorithms, model-based collaborative filtering algorithms, and text mining algorithms. Others have proposed new methods or have built various architectures/frameworks. In this paper, we proposed a new data model based on users' preferences to improve item-based recommendation accuracy by using the Apache Mahout library. We also present details of the implementation of this model on a dataset taken from Amazon. Our experimental results indicate that the proposed model can achieve appreciable improvements in terms of recommendation quality.