Please use this identifier to cite or link to this item:
|Title:||An online video recommendation framework using rich information||Authors:||Zhao, X.
online video recommendation
|Issue Date:||2011||Citation:||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.
checked on Sep 29, 2022
checked on Sep 22, 2022
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.