Incremental SVD-Based Collaborative Filtering Enhanced with Diversity for Personalized Recommendation

Along with the rapid rise of the internet, an e-commerce website brings enormous benefits for both customers and vendors. However, many choices are given at the same time makes customers have difficulty in choosing the most suitable products. A rising star solution for this is the recommender system which helps to narrow down the amount of suitable and relevant products for each customer. Matrix factorization is one of the most popular techniques used in recommender systems because of its effectiveness and simplicity. In this paper, we introduce a matrix factorization-based recommender system using Singular Value Decomposition (SVD) with some improvements in collaborative filtering and incremental learning. The SVD-based collaborative filtering methods can help generate personalized recommendations by combining user profiles. Moreover, the recommendation lists generated by the system are enhanced with diversity, which might attract more customer interests. Amazon’s Electronic data set is used to evaluate our proposed framework of the SVD-based recommender system. The experimental results show that our framework is promising.