Long et al 2014a - Revision history

Scipediacontent at 14:33, 21 January 2021

2021-01-21T14:33:44Z

Scipediacontent: Scipediacontent moved page Draft Content 902849592 to Long et al 2014a

2020-10-14T11:52:09Z

Scipediacontent moved page Draft Content 902849592 to Long et al 2014a

Scipediacontent: Created page with " == Abstract == Predicting popular videos in online social networks (OSNs) is important for network traffic engineering and video recommendation. In order to avoid the diffic..."

2020-10-14T11:52:07Z

Created page with " == Abstract == Predicting popular videos in online social networks (OSNs) is important for network traffic engineering and video recommendation. In order to avoid the diffic..."

New page

== 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 users 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 users diffusion actions as the same. Based on large-scale video diffusion traces collected from a popular OSN, we analyze the positions of users video sharing actions in the propagation graph, and classify users 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.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_902849592-beopen403-6168-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://hub.hku.hk/bitstream/10722/217392/1/Content.pdf http://hub.hku.hk/bitstream/10722/217392/1/Content.pdf]

@@ Line 2: / Line 2: @@
 == 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 users 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 users diffusion actions as the same. Based on large-scale video diffusion traces collected from a popular OSN, we analyze the positions of users video sharing actions in the propagation graph, and classify users 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.
+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.
-−
-Document type: Part of book or chapter of book
-−
-== Full document ==
-<pdf>Media:Draft_Content_902849592-beopen403-6168-document.pdf</pdf>
@@ Line 15: / Line 10: @@
 * [http://hub.hku.hk/bitstream/10722/217392/1/Content.pdf http://hub.hku.hk/bitstream/10722/217392/1/Content.pdf]

← Older revision	Revision as of 11:52, 14 October 2020
(No difference)