Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms
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. O...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2021-09-01
|
| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2021.9020002 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832568953338068992 |
|---|---|
| author | Shuai Zhang Hongyan Liu Jun He Sanpu Han Xiaoyong Du |
| author_facet | Shuai Zhang Hongyan Liu Jun He Sanpu Han Xiaoyong Du |
| author_sort | Shuai Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f709cab4502a4fff9f5b98b5da4f4f67 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2021-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-f709cab4502a4fff9f5b98b5da4f4f672025-02-02T23:47:26ZengTsinghua University PressBig Data Mining and Analytics2096-06542021-09-014317318210.26599/BDMA.2021.9020002Deep Sequential Model for Anchor Recommendation on Live Streaming PlatformsShuai Zhang0Hongyan Liu1Jun He2Sanpu Han3Xiaoyong Du4<institution>School of Information, Renmin University of China</institution>, <city>Beijing</city> <postal-code>100872</postal-code>, <country>China</country><institution>School of Economics and Management, Tsinghua University</institution>, <city>Beijing</city> <postal-code>100084</postal-code>, <country>China</country><institution>School of Information, Renmin University of China</institution>, <city>Beijing</city> <postal-code>100872</postal-code>, <country>China</country><institution>Beijing Mijing Hefeng Technology Co. Ltd.</institution>, <city>Beijing</city> <postal-code>100621</postal-code>, <country>China</country><institution>School of Information, Renmin University of China</institution>, <city>Beijing</city> <postal-code>100872</postal-code>, <country>China</country>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.https://www.sciopen.com/article/10.26599/BDMA.2021.9020002live streamingsequential recommendationattention mechanismdeep learning |
| spellingShingle | Shuai Zhang Hongyan Liu Jun He Sanpu Han Xiaoyong Du Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms Big Data Mining and Analytics live streaming sequential recommendation attention mechanism deep learning |
| title | Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms |
| title_full | Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms |
| title_fullStr | Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms |
| title_full_unstemmed | Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms |
| title_short | Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms |
| title_sort | deep sequential model for anchor recommendation on live streaming platforms |
| topic | live streaming sequential recommendation attention mechanism deep learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2021.9020002 |
| work_keys_str_mv | AT shuaizhang deepsequentialmodelforanchorrecommendationonlivestreamingplatforms AT hongyanliu deepsequentialmodelforanchorrecommendationonlivestreamingplatforms AT junhe deepsequentialmodelforanchorrecommendationonlivestreamingplatforms AT sanpuhan deepsequentialmodelforanchorrecommendationonlivestreamingplatforms AT xiaoyongdu deepsequentialmodelforanchorrecommendationonlivestreamingplatforms |