Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Proceedings of the 16th international conference on World Wide Web
Extracting the discussion structure in comments on news-articles
Proceedings of the 9th annual ACM international workshop on Web information and data management
Comments-oriented document summarization: understanding documents with readers' feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised ranking aggregation
Proceedings of the 17th ACM conference on Information and knowledge management
Ranking Comments on the Social Web
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Towards recency ranking in web search
Proceedings of the third ACM international conference on Web search and data mining
Ranking specialization for web search: a divide-and-conquer approach by using topical RankSVM
Proceedings of the 19th international conference on World wide web
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
Proceedings of the 19th international conference on World wide web
Learning to rank with multiple objective functions
Proceedings of the 20th international conference on World wide web
Statistical ranking and combinatorial Hodge theory
Mathematical Programming: Series A and B - Special Issue on "Optimization and Machine learning"; Alexandre d’Aspremont • Francis Bach • Inderjit S. Dhillon • Bin Yu
Learning to rank for freshness and relevance
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Rank aggregation via nuclear norm minimization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
User reputation in a comment rating environment
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized recommendation of user comments via factor models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Topic-driven reader comments summarization
Proceedings of the 21st ACM international conference on Information and knowledge management
Automatic selection of social media responses to news
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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With the explosion of information on any topic, the need for ranking is becoming very critical. Ranking typically depends on several aspects. Products, for example, have several aspects like price, recency, rating, etc. Product ranking has to bring the "best" product which is recent and highly rated. Hence ranking has to satisfy multiple objectives. In this paper, we explore multi-objective ranking of comments using Hodge decomposition. While Hodge decomposition produces a globally consistent ranking, a globally inconsistent component is also present. We propose an active learning strategy for the reduction of this component. Finally, we develop techniques for online Hodge decomposition. We experimentally validate the ideas presented in this paper.