m (Scipediacontent moved page Draft Content 902849592 to Long et al 2014a) |
|||
| Line 2: | Line 2: | ||
== Abstract == | == Abstract == | ||
| − | Predicting popular videos in online social networks (OSNs) is important for network traffic engineering and video recommendation. In order to avoid the difficulty of acquiring all OSN | + | Predicting popular videos in online social networks (OSNs) is important for network traffic engineering and video recommendation. In order to avoid the difficulty of acquiring all OSN usersâ activities, recent studies try to predict popular media contents in OSNs only based on a very small number of users, referred to as experts. However, these studies simply treat all usersâ diffusion actions as the same. Based on large-scale video diffusion traces collected from a popular OSN, we analyze the positions of usersâ video sharing actions in the propagation graph, and classify usersâ video sharing actions into three different types, i.e., initiator actions, spreader actions and follower actions. Surprisingly, while existing studies mainly focus on the initiators, our empirical studies suggest that the spreaders actually play a more important role in the diffusion process of popular videos. Motivated by this finding, we account for the position information of sharing actions to select initiator experts, spreader experts and follower experts, based on corresponding sharing actions. We conduct experiments on the collected dataset to evaluate the performance of these three types of experts in predicting popular videos. The evaluation results demonstrate that the spreader experts can not only make more accurate predictions than initiator experts and follower experts, but also outperform the general experts selected by existing studies. |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| Line 15: | Line 10: | ||
* [http://hub.hku.hk/bitstream/10722/217392/1/Content.pdf http://hub.hku.hk/bitstream/10722/217392/1/Content.pdf] | * [http://hub.hku.hk/bitstream/10722/217392/1/Content.pdf http://hub.hku.hk/bitstream/10722/217392/1/Content.pdf] | ||
| + | |||
| + | * [http://link.springer.com/content/pdf/10.1007/978-3-319-11746-1_9 http://link.springer.com/content/pdf/10.1007/978-3-319-11746-1_9], | ||
| + | : [http://dx.doi.org/10.1007/978-3-319-11746-1_9 http://dx.doi.org/10.1007/978-3-319-11746-1_9] | ||
| + | |||
| + | * [https://link.springer.com/chapter/10.1007/978-3-319-11746-1_9 https://link.springer.com/chapter/10.1007/978-3-319-11746-1_9], | ||
| + | : [https://core.ac.uk/display/38082206 https://core.ac.uk/display/38082206], | ||
| + | : [https://www.scipedia.com/public/Long_et_al_2014a https://www.scipedia.com/public/Long_et_al_2014a], | ||
| + | : [https://dblp.uni-trier.de/db/conf/wise/wise2014-2.html#LongLN14 https://dblp.uni-trier.de/db/conf/wise/wise2014-2.html#LongLN14], | ||
| + | : [http://hub.hku.hk/bitstream/10722/217392/1/Content.pdf http://hub.hku.hk/bitstream/10722/217392/1/Content.pdf], | ||
| + | : [https://rd.springer.com/chapter/10.1007/978-3-319-11746-1_9 https://rd.springer.com/chapter/10.1007/978-3-319-11746-1_9], | ||
| + | : [http://hub.hku.hk/handle/10722/217392 http://hub.hku.hk/handle/10722/217392], | ||
| + | : [https://academic.microsoft.com/#/detail/187797393 https://academic.microsoft.com/#/detail/187797393] | ||
Latest revision as of 15:33, 21 January 2021
Abstract
Predicting popular videos in online social networks (OSNs) is important for network traffic engineering and video recommendation. In order to avoid the difficulty of acquiring all OSN usersâ activities, recent studies try to predict popular media contents in OSNs only based on a very small number of users, referred to as experts. However, these studies simply treat all usersâ diffusion actions as the same. Based on large-scale video diffusion traces collected from a popular OSN, we analyze the positions of usersâ video sharing actions in the propagation graph, and classify usersâ video sharing actions into three different types, i.e., initiator actions, spreader actions and follower actions. Surprisingly, while existing studies mainly focus on the initiators, our empirical studies suggest that the spreaders actually play a more important role in the diffusion process of popular videos. Motivated by this finding, we account for the position information of sharing actions to select initiator experts, spreader experts and follower experts, based on corresponding sharing actions. We conduct experiments on the collected dataset to evaluate the performance of these three types of experts in predicting popular videos. The evaluation results demonstrate that the spreader experts can not only make more accurate predictions than initiator experts and follower experts, but also outperform the general experts selected by existing studies.
Original document
The different versions of the original document can be found in:
- https://core.ac.uk/display/38082206,
- https://www.scipedia.com/public/Long_et_al_2014a,
- https://dblp.uni-trier.de/db/conf/wise/wise2014-2.html#LongLN14,
- http://hub.hku.hk/bitstream/10722/217392/1/Content.pdf,
- https://rd.springer.com/chapter/10.1007/978-3-319-11746-1_9,
- http://hub.hku.hk/handle/10722/217392,
- https://academic.microsoft.com/#/detail/187797393
Document information
Published on 01/01/2014
Volume 2014, 2014
DOI: 10.1007/978-3-319-11746-1_9
Licence: CC BY-NC-SA license
Share this document
Keywords
claim authorship
Are you one of the authors of this document?