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Big Data Mining and Analytics 2021, 4(3): 173-182 https://doi.org/10.26599/BDMA.2021.9020002
Open Access | Issue | Published: 12 May 2021

Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms

Show Author's Information 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
Keywords:
live streaming, sequential recommendation, attention mechanism, deep learning
Cite this article:
Zhang S, Liu H, He J, et al. Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms. Big Data Mining and Analytics, 2021, 4(3): 173-182. https://doi.org/10.26599/BDMA.2021.9020002
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Abstract Full text About this article

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.


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About this article

Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms

Show Author's information 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.

Keywords: live streaming, sequential recommendation, attention mechanism, deep learning

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Publication history

Received: 27 October 2020
Revised: 16 January 2021
Accepted: 19 January 2021
Published: 12 May 2021
Issue date: September 2021

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© The author(s) 2021

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (NSFC) (Nos. 71771131 and U1711262).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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