Enhancing online video recommendation using social user interactions
Enhancing online video recommendation using social user interactions
复制标题
使用社交用户交互增强在线视频推荐
DOI：
10.1007/s00778-017-0469-2
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发表时间：
2017-06
期刊：
影响因子：
4.2
通讯作者：
Zhou XM
中科院分区：
文献类型：
--
作者：
Zhou Xiangmin;Qin Dong;Chen Lei;Zhang Yanchun;Zhang Yanchun;Cao Longbing;Huang Guangyan;Wang Chen;Zhou XM
媒体共享社区的创建导致了数字视频的惊人增长，以及它们在在线新闻广播、娱乐和广告等领域的广泛应用。这些应用的改进依赖于社交用户访问视频的有效解决方案。这一事实推动了对共享社区中推荐的研究兴趣。虽然人们已经在社交视频推荐方面做出了努力，但是社交用户的上下文信息并没有被很好地利用来进行有效的推荐。基于此，本文提出了一种基于视频内容和用户信息的共享社区推荐方法。提出了一种新的解决方案，允许批量视频推荐给多个新用户，并优化子社区提取。首先，我们提出了一种有效的技术，降低了子图的划分成本的基础上，图分解和重构有效的子社区提取。然后，我们设计了一种基于摘要的算法，该算法对多个未注册用户的点击视频进行分组，并同时向每个用户提供推荐。最后，我们提出了一个非平凡的社会更新维护方法的基础上，用户连接摘要的社会数据。我们评估我们的解决方案的性能在一个大的数据集，考虑不同的策略，在共享社区的组视频推荐。
The creation of media sharing communities has resulted in the astonishing increase of digital videos, and their wide applications in the domains like online news broadcasting, entertainment and advertisement. The improvement of these applications relies on effective solutions for social user access to videos. This fact has driven the research interest in the recommendation in shared communities. Though effort has been put into social video recommendation, the contextual information on social users has not been well exploited for effective recommendation. Motivated by this, in this paper, we propose a novel approach based on the video content and user information for the recommendation in shared communities. A new solution is developed by allowing batch video recommendation to multiple new users and optimizing the subcommunity extraction. We first propose an effective technique that reduces the subgraph partition cost based on graph decomposition and reconstruction for efficient subcommunity extraction. Then, we design a summarization-based algorithm which groups the clicked videos of multiple unregistered users and simultaneously provide recommendation to each of them. Finally, we present a nontrivial social updates maintenance approach for social data based on user connection summarization. We evaluate the performance of our solution over a large dataset considering different strategies for group video recommendation in sharing communities.
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DOI：
10.1109/icip.2002.1038101
发表时间：
2002-12
期刊：
Proceedings. International Conference on Image Processing
影响因子：
--
作者：
S. Cheung;A. Zakhor
通讯作者：
S. Cheung;A. Zakhor
影响因子：
8.9
作者：
Xiangmin Zhou;Xiaofang Zhou;Lei Chen;Y. Shu;A. Bouguettaya;John A. Taylor
通讯作者：
Xiangmin Zhou;Xiaofang Zhou;Lei Chen;Y. Shu;A. Bouguettaya;John A. Taylor
DOI：
10.1145/2009916.2009937
发表时间：
2011-07
期刊：
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
影响因子：
--
作者：
Lei Li;Dingding Wang;Tao Li;Daniel Knox;B. Padmanabhan
通讯作者：
Lei Li;Dingding Wang;Tao Li;Daniel Knox;B. Padmanabhan
DOI：
10.1145/2072298.2071937
发表时间：
2011-11
期刊：
Proceedings of the 19th ACM international conference on Multimedia
影响因子：
--
作者：
Xiangmin Zhou;Lei Chen;Xiaofang Zhou
通讯作者：
Xiangmin Zhou;Lei Chen;Xiaofang Zhou
DOI：
10.1109/icip.2003.1247068
发表时间：
2003-11
期刊：
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)
影响因子：
--
作者：
John R. Smith-;A. Jaimes;Ching-Yung Lin;M. Naphade;A. Natsev;Belle L. Tseng
通讯作者：
John R. Smith-;A. Jaimes;Ching-Yung Lin;M. Naphade;A. Natsev;Belle L. Tseng