TY - GEN
T1 - Social interaction based video recommendation
T2 - 2014 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2014
AU - Nie, Bin
AU - Zhang, Honggang
AU - Liu, Yong
PY - 2014
Y1 - 2014
N2 - Online videos, e.g., YouTube videos, are important topics for social interactions among users of online social networking sites (OSN), e.g., Facebook. This opens up the possibility of exploiting video-related user social interaction information for better video recommendation. Towards this goal, we conduct a case study of recommending YouTube videos to Facebook users based on their social interactions. We first measure social interactions related to YouTube videos among Facebook users. We observe that the attention a video attracts on Facebook is not always well-aligned with its popularity on YouTube. Unpopular videos on YouTube can become popular on Facebook, while popular videos on YouTube often do not attract proportionally high attentions on Facebook. This finding motivates us to develop a simple top-k video recommendation algorithm that exploits user social interaction information to improve the recommendation accuracy for niche videos, that are globally unpopular, but highly relevant to a specific user or user group. Through experiments on the collected Facebook traces, we demonstrate that our recommendation algorithm significantly outperforms the YouTube-popularity based video recommendation algorithm as well as a collaborative filtering algorithm based on user similarities.
AB - Online videos, e.g., YouTube videos, are important topics for social interactions among users of online social networking sites (OSN), e.g., Facebook. This opens up the possibility of exploiting video-related user social interaction information for better video recommendation. Towards this goal, we conduct a case study of recommending YouTube videos to Facebook users based on their social interactions. We first measure social interactions related to YouTube videos among Facebook users. We observe that the attention a video attracts on Facebook is not always well-aligned with its popularity on YouTube. Unpopular videos on YouTube can become popular on Facebook, while popular videos on YouTube often do not attract proportionally high attentions on Facebook. This finding motivates us to develop a simple top-k video recommendation algorithm that exploits user social interaction information to improve the recommendation accuracy for niche videos, that are globally unpopular, but highly relevant to a specific user or user group. Through experiments on the collected Facebook traces, we demonstrate that our recommendation algorithm significantly outperforms the YouTube-popularity based video recommendation algorithm as well as a collaborative filtering algorithm based on user similarities.
UR - http://www.scopus.com/inward/record.url?scp=84904497461&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904497461&partnerID=8YFLogxK
U2 - 10.1109/INFCOMW.2014.6849175
DO - 10.1109/INFCOMW.2014.6849175
M3 - Conference contribution
AN - SCOPUS:84904497461
SN - 9781479930883
T3 - Proceedings - IEEE INFOCOM
SP - 97
EP - 102
BT - 2014 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2014
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 April 2014 through 2 May 2014
ER -