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

References(24)

[1]
X. Chen, T. V. Nguyen, Z. Q. Shen, and M. Kankanhalli, Livesense: Contextual advertising in live streaming videos, in Proc. 27th ACM Int. Conf. on Multimedia, Nice, France, 2019, pp. 392-400.
DOI
[2]
Z. H. Zhu, Z. Yang, and Y. F. Dai, Understanding the gift-sending interaction on live-streaming video websites, in Proc. 9th Int. Conf. on Social Computing and Social Media, Vancouver, Canada, 2017, pp. 274-285.
DOI
[3]
Z. C. Lu, H. J. Xia, S. Heo, and D. Wigdor, You watch, you give, and you engage: A study of live streaming practices in China, in Proc. 2018 CHI Conf. on Human Factors in Computing Systems, Montreal, Canada, 2018, pp. 1-13.
DOI
[4]
Y. Koren, R. Bell, and C. Volinsky, Matrix factorization techniques for recommender systems, Computer, vol. 42, no. 8, pp. 30-37, 2009.
DOI Google Scholar
[5]
R. N. He and J. McAuley, Fusing similarity models with markov chains for sparse sequential recommendation, in Proc. 2016 IEEE 16th Int. Conf. on Data Mining, Barcelona, Spain, 2016, pp. 191-200.
DOI
[6]
F. Yu, Q. Liu, S. Wu, L. Wang, and T. N. Tan, A dynamic recurrent model for next basket recommendation, in Proc. 39th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, Pisa, Italy, 2016, pp. 729-732.
DOI
[7]
B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, Session-based recommendations with recurrent neural networks, in Proc. 4th Int. Conf. on Learning Representations, San Juan, Puerto Rico, 2016.
[8]
Y. K. Tan, X. X. Xu, and Y. Liu, Improved recurrent neural networks for session-based recommendations, in Proc. 1st Workshop on Deep Learning for Recommender Systems, Boston, MA, USA, 2016, pp. 17-22.
DOI
[9]
C. Y. Wu, A. Ahmed, A. Beutel, A. J. Smola, and H. Jing, Recurrent recommender networks, in Proc. 10th ACM Int. Conf. on Web Search and Data Mining, Cambridge, UK, 2017, pp. 495-503.
DOI
[10]
Z. Li, H. K. Zhao, Q. Liu, Z. Y. Huang, T. Mei, and E. H. Chen, Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors, in Proc. 24th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, London, UK, 2018, pp. 1734-1743.
DOI
[11]
X. Chen, H. T. Xu, Y. F. Zhang, J. X. Tang, Y. X. Cao, Z. Qin, and H. Y. Zha, Sequential recommendation with user memory networks, in Proc. 11th ACM Int. Conf. on Web Search and Data Mining, Marina Del Rey, CA, USA, 2018, pp. 108-116.
DOI
[12]
J. Huang, W. X. Zhao, H. J. Dou, J. R. Wen, and E. Y. Chang, Improving sequential recommendation with knowledge-enhanced memory networks, in Proc. 41st Int. ACM SIGIR Conf. on Research & Development in Information Retrieval, Ann Arbor, MI, USA, 2018, pp. 505-514.
DOI
[13]
M. Z. Zhou, Z. Y. Ding, J. L. Tang, and D. W. Yin, Micro behaviors: A new perspective in e-commerce recommender systems, in Proc. 11th ACM Int. Conf. on Web Search and Data Mining, Marina Del Rey, CA, USA, 2018, pp. 727-735.
DOI
[14]
H. C. Ying, F. Z. Zhuang, F. Z. Zhang, Y. C. Liu, G. D. Xu, X. Xie, H. Xiong, and J. Wu, Sequential recommender system based on hierarchical attention network, in Proc. 27th Int. Joint Conf. on Artificial Intelligence, Stockholm, Sweden, 2018, pp. 3926-3932.
DOI
[15]
Q. Liu, Y. F. Zeng, R. Mokhosi, and H. B. Zhang, Stamp: Short-term attention/memory priority model for session-based recommendation, in Proc. 24th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, London, UK, 2018, pp. 1831-1839.
DOI
[16]
L. Yu, C. X. Zhang, S. S. Liang, and X. L. Zhang, Multi-order attentive ranking model for sequential recommendation, in Proc. 33rd AAAI Conf. on Artificial Intelligence, Honolulu, HI, USA, 2019, pp. 5709-5716.
DOI
[17]
S. Wu, Y. Y. Tang, Y. Q. Zhu, L. Wang, X. Xie, and T. J. Tan, Session-based recommendation with graph neural networks, in Proc. 33rd AAAI Conf. on Artificial Intelligence, Honolulu, HI, USA, 2019, pp. 346-353.
DOI
[18]
T. T. Zhang, P. P. Zhao, Y. C. Liu, V. S. Sheng, J. J. Xu, D. Q. Wang, G. F. Liu, and X. F. Zhou, Feature-level deeper self-attention network for sequential recommendation, in Proc. 28th Int. Joint Conf. on Artificial Intelligence, Macao, China, 2019, pp. 4320-4326.
DOI
[19]
J. L. Wang and J. Caverlee, Recurrent recommendation with local coherence, in Proc. 12th ACM Int. Conf. on Web Search and Data Mining, Melbourne, Australia, 2019, pp. 564-572.
DOI
[20]
J. C. Li, Y. J. Wang, and J. McAuley, Time interval aware self-attention for sequential recommendation, in Proc. 13th Int. Conf. on Web Search and Data Mining, Houston, TX, USA, 2020, pp. 322-330.
DOI
[21]
T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, in Proc. 1st Int. Conf. on Learning Representations, Scottsdale, AZ, USA, 2013.
[22]
S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
DOI Google Scholar
[23]
S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, BPR: Bayesian personalized ranking from implicit feedback, in Proc. 25th Conf. on Uncertainty in Artificial Intelligence, Montreal, Canada, 2009, pp. 452-461.
[24]
J. X. Tang and K. Wang, Personalized top-n sequential recommendation via convolutional sequence embedding, in Proc. 11th ACM Int. Conf. on Web Search and Data Mining, Marina Del Rey, CA, USA, 2018, pp. 565-573.
DOI
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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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