Big Data Mining and Analytics  2021, Vol. 4 Issue (3): 173-182    DOI: 10.26599/BDMA.2021.9020002
Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms
Shuai Zhang1(),Hongyan Liu2,*(),Jun He1,*(),Sanpu Han3(),Xiaoyong Du1()
School of Information, Renmin University of China, Beijing 100872, China
School of Economics and Management, Tsinghua University, Beijing 100084, China
Beijing Mijing Hefeng Technology Co. Ltd., Beijing 100621, China

Abstract

Live streaming has grown rapidly in recent years, attracting increasingly more participation. As the number of online anchors is large, it is difficult for viewers to find the anchors they are interested in. Therefore, a personalized recommendation system is important for live streaming platforms. On live streaming platforms, the viewer’s and anchor’s preferences are dynamically changing over time. How to capture the user’s preference change is extensively studied in the literature, but how to model the viewer’s and anchor’s preference changes and how to learn their representations based on their preference matching are less studied. Taking these issues into consideration, in this paper, we propose a deep sequential model for live streaming recommendation. We develop a component named the multi-head related-unit in the model to capture the preference matching between anchor and viewer and extract related features for their representations. To evaluate the performance of our proposed model, we conduct experiments on real datasets, and the results show that our proposed model outperforms state-of-the-art recommendation models.

Received: 27 October 2020      Published: 20 May 2021
Fund:  National Natural Science Foundation of China (NSFC)(71771131)
Corresponding Authors: Hongyan Liu,Jun He     E-mail: zhangshuai_2017@ruc.edu.cn;liuhy@sem.tsinghua.edu.cn;hejun@ruc.edu.cn;hansanpu@360.cn;duyong@ruc.edu.cn
About author: Shuai Zhang received the MS degree from Peking University, China, in 2017. He is currently pursuing the PhD degree under the guidance of Dr. Jun He at the School of Information, Renmin University of China, China. His research interests include deep learning and recommendation systems.|Hongyan Liu is a professor at the School of Economics and Management, Tsinghua University. She received the PhD degree in management science from Tsinghua University. Her current research interests include data/text mining, personalized recommendation, social computing, and medical and financial data analytics. She has published many papers in top journals, such as MISQ, ISR, INFORMS JOC, ACM TODS, ACM TOIS, and IEEE TKDE, and in top conferences, such as VLDB, ICDE, SIGKDD, ICDM, SDM, CIKM, and ICIS.|Jun He received the PhD degree in computer science from Renmin University of China, where he is now a professor and PhD supervisor. His current research interests include data mining, social network analysis, recommendation systems, and computer vision. He has published papers in many international conferences such as ACM SIGKDD, IEEE ICDM, SIAM on Data Mining, ACM CIKM, and Pacific Graphics, and in journals, such as the ACM TOIS and IEEE TKDE. He is a member of the IEEE, ACM, and AIS.|Sanpu Han is the co-founder and CTO of the Huajiao live streaming platform. He received the master degrees from Tsinghua University and Peking University in 2015 and 2010, respectively. His research interests include personalized recommendation.|Xiaoyong Du received the PhD degree from Nagoya Institute of Technology, Japan in 1997. He was an assistant professor at the Department of Intelligence and Computer Science, Nagoya Institute of Technology, from 1997 to 1999. Since 1999, he has been a professor at the School of Information, Renmin University of China. His current research interests include high performance databases, intelligent information retrieval, data mining, and the semantic web. He has published more than 100 peer-reviewed papers in journals and conferences.