NDLI: On "one of the few" objects

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On "one of the few" objects

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On "one of the few" objects

Content Provider	ACM Digital Library
Author	Agarwal, Pankaj K. Li, Chengkai Yu, Cong Yang, Jun Wu, You
Abstract	Objects with multiple numeric attributes can be compared within any "subspace" (subset of attributes). In applications such as computational journalism, users are interested in claims of the form: Karl Malone is one of the only two players in NBA history with at least 25,000 points, 12,000 rebounds, and 5,000 assists in one's career. One challenge in identifying such "one-of-the-k" claims (k = 2 above) is ensuring their "interestingness". A small k is not a good indicator for interestingness, as one can often make such claims for many objects by increasing the dimensionality of the subspace considered. We propose a uniqueness-based interestingness measure for one-of-the-few claims that is intuitive for non-technical users, and we design algorithms for finding all interesting claims (across all subspaces) from a dataset. Sometimes, users are interested primarily in the objects appearing in these claims. Building on our notion of interesting claims, we propose a scheme for ranking objects and an algorithm for computing the top-ranked objects. Using real-world datasets, we evaluate the efficiency of our algorithms as well as the advantage of our object-ranking scheme over popular methods such as Kemeny optimal rank aggregation and weighted-sum ranking.
Starting Page	1487
Ending Page	1495
Page Count	9
File Format	PDF MP4
ISBN	9781450314626
DOI	10.1145/2339530.2339762
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-08-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ranking Skyband Fact checking Computational journalism
Content Type	Audio Text
Resource Type	Article

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