Patent
Learning a document ranking function using fidelity-based error measurements
Tie-Yan Liu,Ming-Feng Tsai,Wei-Ying Ma +2 more
- 31 Jul 2006
21
TL;DR: In this article, a method and a system for generating a ranking function using a fidelity-based loss between a target probability and a model probability for a pair of documents is provided.
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Abstract: A method and system for generating a ranking function using a fidelity-based loss between a target probability and a model probability for a pair of documents is provided. A fidelity ranking system generates a fidelity ranking function that ranks the relevance of documents to queries. The fidelity ranking system operates to minimize a fidelity loss between pairs of documents of training data. The fidelity loss may be derived from “fidelity” as used in the field of quantum physics. The fidelity ranking system may use a learning technique in conjunction with a fidelity loss when generating the ranking function. After the fidelity ranking system generates the fidelity ranking function, it uses the fidelity ranking function to rank the relevance of documents to queries.
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Citations
Patent
Learning to rank using query-dependent loss functions
Tie-Yan Liu
- 01 Apr 2009
TL;DR: In this paper, the authors proposed a technique that incorporates query difference into learning to rank by introducing query-dependent loss functions, which employs query categorization to represent query differences and employs specific query dependent loss functions based on such kind of query differences.
6
Patent
Search result ranker
Pavel Serdyukov,Yury Ustinovskiy,Gleb Gusev +2 more
- 08 Dec 2014
TL;DR: In this article, a computerized method for optimizing search result rankings obtained from a search result ranker has been proposed, which retrieves a first set of query-document pairs, each query- document pair of the first set having an associated post-impression features vector.
5
Patent
Decomposable ranking for efficient precomputing that selects preliminary ranking features comprising static ranking features and dynamic atom-isolated components
Knut Magne Risvik,Michael Hopcroft,John G. Bennett,Karthik Kalyanaraman,Trishul Chilimbi,Vishesh M. Parikh +5 more
- 22 Nov 2010
TL;DR: In this article, a final ranking function that provides final rankings for documents is analyzed to identify potential preliminary ranking features, such as static ranking features that are query independent and dynamic atom-isolated components that are related to a single atom.
2
Patent
Partitioned distributed database systems, devices, and methods
Graham Carlos Sanderson,Benedict John Elliot Smith +1 more
- 11 Oct 2018
TL;DR: In this article, a client system can improve processing speeds by executing queries locally using templates for specialized expression evaluators, and queries can be executed using templates using specialized evaluator templates.
2
Query dependent ranking using K-nearest neighbor
Xiubo Geng,Tie-Yan Liu,Tao Qin,Andrew Arnold,Hang Li,Heung-Yeung Shum +5 more
- 20 Jul 2008
TL;DR: This paper proposes a K-Nearest Neighbor (KNN) method for query-dependent ranking, and proves a theory which indicates that the approximations are accurate in terms of difference in loss of prediction, if the learning algorithm used is stable with respect to minor changes in training examples.
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Patent
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