Information retrieval system evaluation: effort, sensitivity, and reliability
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Usage patterns of collaborative tagging systems
Journal of Information Science
Journal of the American Society for Information Science and Technology
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Social tags: meaning and suggestions
Proceedings of the 17th ACM conference on Information and knowledge management
Improving Search and Navigation by Combining Ontologies and Social Tags
OTM '08 Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWS
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Improving tag recommendation using social networks
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Journal of Web Engineering
Leveraging collaborative tagging for web item design
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Information and knowledge management in online rich presence services
Information Systems Frontiers
| Hi-index | 0.00 |
A great many tags and videos are shared and created by a mass of distributors on Web 2.0 video sharing sites. This increasing user-generated content can further benefit service innovation of collaborative tagging. In order to enhance efficient video retrieval and online video marketing (OVM) application, this research proposes a rank-mediated collaborative tagging recommendation service that allows the distributors predicting the ranks of video retrieval from the shared video archive using vote-promotion algorithm (VPA). The system experiments evaluate the number of tags and videos between simple text retrieval and VPA. The user surveys verify the relevance, helpfulness, and satisfaction of the recommended tags. From the perspectives of service innovation, this research is to develop a systematic and quantified a video-tag relationship prediction and recommendation self-service that can provide an intelligent collaborative tagging service on video sharing sites.