Please use this identifier to cite or link to this item: https://doi.org/10.1145/2043674.2043688
Title: An online video recommendation framework using rich information
Authors: Zhao, X.
Li, G. 
Wang, M. 
Li, S.
Chen, X.
Li, Z.
Keywords: multimodal similarity
online video recommendation
viewing history
Issue Date: 2011
Source: Zhao, X.,Li, G.,Wang, M.,Li, S.,Chen, X.,Li, Z. (2011). An online video recommendation framework using rich information. ACM International Conference Proceeding Series : 46-49. ScholarBank@NUS Repository. https://doi.org/10.1145/2043674.2043688
Abstract: Automatic video recommendation is involved in an attempt to tackle the information-overload problem, aiming to present the personalized video list to the user. This paper presents a novel approach to improve the accuracy of the video recommendation by combining the content-based filtering (CBF) method and the collaborative filtering (CF) method. Multimodal information is utilized to calculate the similarity among different videos to overcome the sparseness problem by CF method. We conduct experiments on a dataset of more than 11,000 videos and the results demonstrate the feasibility and effectiveness of our approach. © 2011 ACM.
Source Title: ACM International Conference Proceeding Series
URI: http://scholarbank.nus.edu.sg/handle/10635/40208
ISBN: 9781450309189
DOI: 10.1145/2043674.2043688
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

2
checked on Dec 11, 2017

Page view(s)

59
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.