Results 211 to 220 of about 1,430 (254)
Complementing Büchi Automata with Ranker
Lecture Notes in Computer Science, 2022 AbstractWe present the toolRankerfor complementing Büchi automata (BAs).Rankerbuilds on our previous optimizations of rank-based BA complementation and pushes them even further using numerous heuristics to produce even smaller automata. Moreover, it contains novel optimizations of specialized constructions for complementing (i) inherently weak automata
Ondřej Lengál +2 more
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Multilabel Ranking with Inconsistent Rankers
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021While most existing multilabel ranking methods assume the availability of a single objective label ranking for each instance in the training set, this paper deals with a more common case where only subjective inconsistent rankings from multiple rankers are associated with each instance. Two ranking methods are proposed from the perspective of instances
Xin Geng 0001 +3 more
openaire +2 more sources
Cooperative Retriever and Ranker in Deep Recommenders
Proceedings of the ACM Web Conference 2023, 2023 Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency; while the ranker, usually more ...
Jin Chen, Defu Lian, Enhong Chen
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2011
At the heart of many effective approaches to the core information retrieval problem-identifying relevant content-lies the following three-fold strategy: obtaining content based matches, inferring additional ranking criteria and constraints, and combining all of the above so as to arrive at a single ranking of retrieval units.
Katja Hofmann +2 more
openaire +3 more sources
At the heart of many effective approaches to the core information retrieval problem-identifying relevant content-lies the following three-fold strategy: obtaining content based matches, inferring additional ranking criteria and constraints, and combining all of the above so as to arrive at a single ranking of retrieval units.
Katja Hofmann +2 more
openaire +3 more sources
Different Rankers on Different Subcollections
2015Recent work has shown that when documents in a TREC ad hoc collection are partitioned, different rankers will perform optimally on different partitions. This result suggests that choosing different highly effective rankers for each partition and merging the results, should be able to improve overall effectiveness.
Jones, Timothy +4 more
openaire +2 more sources
Multilabel Ranking with Inconsistent Rankers
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014While most existing multilabel ranking methods assume the availability of a single objective label ranking for each instance in the training set, this paper deals with a more common case where subjective inconsistent rankings from multiple rankers are associated with each instance.
Xin Geng, Longrun Luo
openaire +1 more source
Neurocomputing, 2016
Abstract Social Network Ranker (SoNeR) is software that retrieves, from the Semantic Web, documents describing people (users) and ranks them based on their popularity. It provides supportive tools for obtaining the related FOAF/RDF documents, parsing them, detecting identity synonyms and ranking people.
Gajo Petrovic, Hamido Fujita
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Abstract Social Network Ranker (SoNeR) is software that retrieves, from the Semantic Web, documents describing people (users) and ranks them based on their popularity. It provides supportive tools for obtaining the related FOAF/RDF documents, parsing them, detecting identity synonyms and ranking people.
Gajo Petrovic, Hamido Fujita
openaire +1 more source
Multiple Ranker Method in Document Retrieval
Communications in Computer and Information Science, 2008In this paper, we propose a multiple-ranker approach to make learning to rank methods more effective for document retrieval application. In traditional learning to rank methods, a ranker is learned from a set of queries together with their corresponding document rankings labeled by experts, and it is then used to predict the document rankings for new ...
Weijian Ni, Yalou Huang, Xie Maoqiang
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