Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Semi-supervised ranking aggregation
Proceedings of the 17th ACM conference on Information and knowledge management
Visual diversification of image search results
Proceedings of the 18th international conference on World wide web
Ranking and classifying attractiveness of photos in folksonomies
Proceedings of the 18th international conference on World wide web
Image ranking based on user browsing behavior
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Many existing ranking-related information processing applications can be summarized into one theoretical problem called group ranking (GR). A simple average-ranking approach is usually applied to GR. Although the approach seems reasonable, no theoretical analysis about its intrinsic mechanism has been presented, increasing the difficulty of evaluating the ranking results. This study provides a formal analysis for GR. We first construct an objective function for the GR problem, and discover that each GR problem can be transformed into a rank aggregation problem whose objective function is proved to be equal to the objective function of GR. As a consequence, the average-ranking approach can be explained by two well-known rank aggregation techniques. We incorporate two other effective rank aggregation methods into the GR problem and obtain two new GR algorithms. We apply the GR algorithms into image retrieval to diversify the image search results returned by search engines. Experimental results show the effectiveness of the proposed GR algorithms.