Research on Improved Collaborative Filtering-Based Mobile E-Commerce Personalized Recommender System

With the development of mobile communication technology and the constant improvement in e-commerce market environment, mobile e-commerce are becoming the new growth point. After the brief introduction of mobile e-commerce personalized recommender system concept, the architecture of MEC-PRS is put forward. Algorithm of neighbor-based collaborative filtering and item rating based collaborative filtering are analyzed and compared emphatically. We found that item rating prediction based collaborative filtering recommendation algorithm can improve the recommend quality of PRS in performance test, and collaborative filtering recommendation algorithm based on item rating prediction provides better recommendation results than traditional collaborative filtering algorithms.