Clothing Recommendation System Based on Advanced User-Based Collaborative Filtering Algorithm

With the development of e-commerce technology, a growing number of people prefer to purchase clothes on the e-commerce websites. Therefore, an effective recommendation system is necessary for customers. User-based Collaborative Filtering (UCF) algorithm is widely utilized to predict the preferences of customers. However, UCF algorithm employs the sparse matrix and the recommendation has low precision. In this paper, an improved recommendation algorithm named Advanced User-based Collaborative Filtering (AUCF) algorithm is proposed and implemented in the clothing recommendation system. The proposed AUCF algorithm introduces user-item linked list, which can overcome the problem of large time complexity. Considering the impact of different popularity of items, AUCF algorithm is capable of publishing the negative influence of popular items, which can increase the recommendation coverage. Experiment results show the AUCF algorithm significantly increases the recommendation coverage and precision.