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» Maximum Margin Ranking Algorithms for Information Retrieval
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IR
2010
13 years 6 months ago
Gradient descent optimization of smoothed information retrieval metrics
Abstract Most ranking algorithms are based on the optimization of some loss functions, such as the pairwise loss. However, these loss functions are often different from the criter...
Olivier Chapelle, Mingrui Wu
AAAI
2011
12 years 7 months ago
Markov Logic Sets: Towards Lifted Information Retrieval Using PageRank and Label Propagation
Inspired by “GoogleTM Sets” and Bayesian sets, we consider the problem of retrieving complex objects and relations among them, i.e., ground atoms from a logical concept, given...
Marion Neumann, Babak Ahmadi, Kristian Kersting
KDD
2010
ACM
318views Data Mining» more  KDD 2010»
13 years 6 months ago
DivRank: the interplay of prestige and diversity in information networks
Information networks are widely used to characterize the relationships between data items such as text documents. Many important retrieval and mining tasks rely on ranking the dat...
Qiaozhu Mei, Jian Guo, Dragomir R. Radev
SIGIR
2011
ACM
12 years 10 months ago
Parallel learning to rank for information retrieval
Learning to rank represents a category of effective ranking methods for information retrieval. While the primary concern of existing research has been accuracy, learning efficien...
Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady Wiraw...
SIGIR
2010
ACM
13 years 11 months ago
Probabilistic latent maximal marginal relevance
Diversity has been heavily motivated in the information retrieval literature as an objective criterion for result sets in search and recommender systems. Perhaps one of the most w...
Shengbo Guo, Scott Sanner