文献类型：会议论文

英文题名：Personalized Video Recommendation Integrating User Portrait and Collaborative Filtering

作者：Cheng, Shuangni[1];Liu, Miao[1];Cao, Wanjing[1]

机构：[1]East China Univ Sci & Technol, Sch Art Design & Media, ECUST, 130 Meilong Rd, Shanghai 200237, Peoples R China

会议论文集：12th Int Conf on Appl Human Factors and Ergon (AHFE) / Virtual Conf on Usabil and User Experience, Human Factors and Wearable Technologies, Human Factors in Virtual Environm and Game Design, and Human Factors and Assist Technol

会议日期：JUL 25-29, 2021

会议地点：ELECTR NETWORK

语种：英文

外文关键词：User portrait; Video playback platform; Personalized recommendation; Collaborative filtering

摘要：In order to improve the quality of recommendation and user-perceived service, this paper constructs a personalized recommendation model that integrates user portraits and collaborative filtering. First, build a portrait label system based on user characteristics, use time decay function and TF-IDF to obtain label weights, calculate user similarity through user feature labels, and merge it with user similarity obtained by user-based collaborative filtering algorithm to reconcile the weights. Obtain the comprehensive similarity of users, then take Top-N in descending order to form the final personalized recommendation. This paper conducts experimental verification through Douban website, and uses offline experiments to prove that compared with a single algorithm, a video personalized recommendation model that combines user portraits and collaborative filtering algorithms can improve the quality of personalized recommendations to a certain extent.