Keyword

candidate generation

Description: The computational process of producing a set of possible entities, items, or solutions for later ranking or evaluation.

Primary topic: Data Mining Algorithms and Applications (46%)

OpenAlex ID: keywords/candidate-generation

Deep Neural Networks for YouTube Recommendations

2016 · Paul Covington, Jay Adams, et al. · ACM Conference on Recommender Systems (RecSys) · 3,533

PDF

Mining frequent patterns without candidate generation

2000 · Jiawei Han, Jian Pei, et al. · Proceedings - ACM-SIGMOD International Conference on Management of Data · 3,217

PDF

gSpan: graph-based substructure pattern mining

2003 · Xifeng Yan, Jiawei Han · Proceedings · 2,062

CLOSET : An Efficient Algorithm for Mining Frequent Closed Itemsets

2000 · Jian Pei · 826

Mining high utility itemsets without candidate generation

2012 · Mengchi Liu, Junfeng Qu · ACM International Conference on Information and Knowledge Management (CIKM) · 671

Finding Frequent Patterns in a Large Sparse Graph*

2005 · Michihiro Kuramochi, George Karypis · Data Mining and Knowledge Discovery · 373

A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations

2014 · Holger R. Roth, Le Lu, et al. · Lecture notes in computer science · 340

PDF

Large Language Models are Zero-Shot Rankers for Recommender Systems

2024 · Yupeng Hou, Junjie Zhang, et al. · Lecture notes in computer science · 321

PDF

kReverse NN Search in Arbitrary Dimensionality

2004 · Yufei Tao, Dimitris Papadias, et al. · Elsevier eBooks · 271

Apriori-based frequent itemset mining algorithms on MapReduce

2012 · Ming-Yen Lin, Pei-Yu Lee, et al. · International Conference on Ubiquitous Information Management and Communication (IMCOM) · 234

A Fast Apriori Implementation

2003 · Ferenc Bodon · 234

Design Challenges for Entity Linking

2015 · Xiao Ling, Sameer Singh, et al. · Transactions of the Association for Computational Linguistics · 219

PDF

Incremental mining of frequent patterns without candidate generation or support constraint

2003 · W. Cheung, O.R. Zaiane · Proceedings - International Database Engineering and Applications Symposium · 217

Relational Inference for Wikification

2013 · Xiao Cheng, Dan Roth · Conference on Empirical Methods in Natural Language Processing (EMNLP) · 197

PDF

Direct Discovery of High Utility Itemsets without Candidate Generation

2012 · Junqiang Liu, Ke Wang, et al. · Proceedings · 187

CIM

2014 · Yi-Cheng Chen, Wen-Yuan Zhu, et al. · ACM Transactions on Intelligent Systems and Technology · 180

Error-tolerant Finite State Recognition with Applications to Morphological Analysis and Spelling Correction

1995 · Oflazer, Kemal · arXiv (Cornell University) · 173

PDF

HUOPM: High-Utility Occupancy Pattern Mining

2019 · Wensheng Gan, Jerry Chun-Wei Lin, et al. · IEEE Transactions on Cybernetics · 163

PDF

Interactive beautification

1997 · Takeo Igarashi, Satoshi Matsuoka, et al. · ACM Symposium on User Interface Software and Technology (UIST) · 149

PDF

Neural entity linking: A survey of models based on deep learning

2022 · Özge Sevgili, Artem Shelmanov, et al. · Semantic Web · 146

PDF

DGCN: Diversified Recommendation with Graph Convolutional Networks

2021 · Yu Zheng, Chen Gao, et al. · 139

An order-clique-based approach for mining maximal co-locations

2009 · Lizhen Wang, Lihua Zhou, et al. · Information Sciences · 130

PDF

Index-BitTableFI: An improved algorithm for mining frequent itemsets

2008 · Wei Song, Bingru Yang, et al. · Knowledge-Based Systems · 125

Lexicon in Focus

2000 · De Gruyter eBooks · 120

International Journal of Computer Trends and Technology

2014 · International Journal of Computer Trends and Technology · 112

Works count: 1,261

Citation count: 24,400