1. Hybrid User-Item Based Collaborative Filtering
- Author
-
Zhenzhen Fan and Nitin Pradeep Kumar
- Subjects
Computer science ,GA ,Recommender system ,SOM ,computer.software_genre ,CBR ,Data set ,Face (geometry) ,Genetic algorithm ,Scalability ,Collaborative filtering ,General Earth and Planetary Sciences ,Case-based reasoning ,Data mining ,Item based collaborative Filtering ,Cluster analysis ,computer ,General Environmental Science - Abstract
Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative filtering (CF) algorithms face two major challenges: data sparsity and scalability. In this study, we propose a hybrid method based on item based CF trying to achieve a more personalized product recommendation for a user while addressing some of these challenges. Case Based Reasoning (CBR) combined with average filling is used to handle the sparsity of data set, while Self-Organizing Map (SOM) optimized with Genetic Algorithm (GA) performs user clustering in large datasets to reduce the scope for item-based CF. The proposed method shows encouraging results when evaluated and compared with the traditional item based CF algorithm.
- Published
- 2015