A Personalized Recommender System Based on Explanation Facilities Using Collaborative Filtering

Collaborative filtering (CF) is the most successful recommendation method, but its widespread use has exposed some limitations, such as sparsity, scalability, and black box. Many researchers have focused on sparsity and scalability problem but a little has tried to solve the black box problem. Most CF recommender systems are black boxes, providing no transparency into the working of the recommendation. This research suggests an improved CF recommender system with explanation facilities to overcome the black box problem. Explanation facilities make it possible to expose the reasoning and data behind a recommendation. Therefore, explanations provide us with a mechanism for handling errors that come with a recommendation. Furthermore, it is proposed to use web usage mining and product taxonomy to enhance the recommendation quality for e-commerce environment. For such purposes, it is developed a recommender system named WebCF-Exp, Web usage mining driven Collaborative Filtering with Explanation facilities. To test the performance of WebCF-Exp, EBIB research internet shopping mall and explanation interfaces are developed. Experiments are conducted with the data provided by EBIB Research Internet shopping mall