Haishuai Wang (with Zhenyan Ji, Zhi Zhang Canzhen Zhou) is a contributing author, " A Fast Interactive Item-Based Collaborative Filtering Algorithm."

Book Introduction:

This book constitutes the thoroughly refereed proceedings of the National Conference of Theoretical Computer Science, NCTCS 2017, held in Wuhan, Hubei, China, in October 2017. The 25 full papers presented were carefully reviewed and selected from 84 submissions. They present relevant trends of current research in the area of algorithms and complexity, software theory and method, data science and machine learning theory.

Paper abstract:

A recommender system becomes more and more popular in e-commerce. Usually prediction results cannot satisfy users’ requirements fully, and sometimes it even contains totally irrelevant items. To reflect users’ newest preference and increase the quality of recommendation, a fast interactive item-based collaborative filtering algorithm is proposed. Firstly, we propose an item-based collaborative filtering algorithm with less time and space complexity. Then we introduce interactive iterations to reflect users’ up-to-date preference and increase users’ satisfaction. The experiments show that our fast interactive item-based CF algorithm has better recall and precision than traditional item-based CF algorithm.