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 e-commerce 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.