Personalized User Interface Elements Recommendation System

Hao Liu, Xiangxian Li, Wei Gai, Yu Huang, Jingbo Zhou, Chenglei Yang

Abstract: This paper introduces a personalized user interface element recommendation system, in which the model can recommend personalized user interface elements by introducing user features and user evaluations in the offline training. Through experiments, we found that compared with common machine learning algorithms, the Field-aware Factorization Machine that introduced user feature intersections has achieved a better accuracy in the recommendation, which shows the advantages of introducing user features and feature intersections in the recommendation of interface elements.