Modern Information Retrieval
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
MultiTube--Where Web 2.0 and Multimedia Could Meet
IEEE MultiMedia
Online video recommendation based on multimodal fusion and relevance feedback
Proceedings of the 6th ACM international conference on Image and video retrieval
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Tag-based social interest discovery
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Exploring Feedback Models in Interactive Tagging
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Improving automatic music tag annotation using stacked generalization of probabilistic SVM outputs
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Predicting social-tags for cold start book recommendations
Proceedings of the third ACM conference on Recommender systems
Social media recommendation based on people and tags
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Relevance feedback based on genetic programming for image retrieval
Pattern Recognition Letters
Demand-driven tag recommendation
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Functional matrix factorizations for cold-start recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Associative tag recommendation exploiting multiple textual features
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A collaborative filtering approach to mitigate the new user cold start problem
Knowledge-Based Systems
Semi-supervised tag recommendation - using untagged resources to mitigate cold-start problems
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Assessing the quality of textual features in social media
Information Processing and Management: an International Journal
| Hi-index | 0.00 |
Tag recommendation methods that exploit co-occurrence patterns of tags have consistently produced state of the art results. However, tags are not present in significant portions of Web 2.0 objects, which may impact the effectiveness of such methods. This problem, known as cold start, is the focus of this paper. We here evaluate the impact of the cold start on a family of methods for recommending tags. Our results show that the effectiveness of these methods suffer greatly when they cannot rely on previously assigned tags in the target object and that the use of automatic filtering strategies to alleviate the problem yields limited gains. We then propose a new strategy that exploits both positive and negative relevance feedback (RF) from the users to iteratively select input tags to these methods. The results show that the proposed strategy generates significant gains (up to 45%) over the best considered baseline. It is also shown that the proposed method is robust to the lack of user cooperation.