scholarly journals RankMerging: a supervised learning-to-rank framework to predict links in large social networks

2019 ◽  
Vol 108 (10) ◽  
pp. 1729-1756 ◽  
Author(s):  
Lionel Tabourier ◽  
Daniel F. Bernardes ◽  
Anne-Sophie Libert ◽  
Renaud Lambiotte
Keyword(s):  
Social Networks ◽  
Supervised Learning ◽  
Learning To Rank

Semi-supervised Learning to Rank with Uncertain Data

2019 ◽  
pp. 28-39
Author(s):  
Xin Zhang ◽  
ZhongQi Zhao ◽  
ChengHou Liu ◽  
Chen Zhang ◽  
Zhi Cheng
Keyword(s):  
Supervised Learning ◽  
Learning To Rank ◽  
Uncertain Data

Clustering-Based Transductive Semi-Supervised Learning for Learning-to-Rank

2019 ◽  
Vol 33 (12) ◽  
pp. 1951007 ◽  
Author(s):  
Ashwini Rahangdale ◽  
Shital Raut
Keyword(s):  
Supervised Learning ◽  
Learning To Rank ◽  
Cost Effective ◽  
Unlabeled Data ◽  
Training Data ◽  
Density Region ◽  
The Arts ◽  
Proposed Model ◽  
Supervised Learning Algorithms ◽  
Multiple Loss

Learning-to-rank (LTR) is a very hot topic of research for information retrieval (IR). LTR framework usually learns the ranking function using available training data that are very cost-effective, time-consuming and biased. When sufficient amount of training data is not available, semi-supervised learning is one of the machine learning paradigms that can be applied to get pseudo label from unlabeled data. Cluster and label is a basic approach for semi-supervised learning to identify the high-density region in data space which is mainly used to support the supervised learning. However, clustering with conventional method may lead to prediction performance which is worse than supervised learning algorithms for application of LTR. Thus, we propose rank preserving clustering (RPC) with PLocalSearch and get pseudo label for unlabeled data. We present semi-supervised learning that adopts clustering-based transductive method and combine it with nonmeasure specific listwise approach to learn the LTR model. Moreover, each cluster follows the multi-task learning to avoid optimization of multiple loss functions. It reduces the training complexity of adopted listwise approach from an exponential order to a polynomial order. Empirical analysis on the standard datasets (LETOR) shows that the proposed model gives better results as compared to other state-of-the-arts.

scholarly journals Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank

2019 ◽  
Vol 41 (8) ◽  
pp. 1862-1878 ◽  
Author(s):  
Xialei Liu ◽  
Joost van de Weijer ◽  
Andrew D. Bagdanov
Keyword(s):  
Supervised Learning ◽  
Learning To Rank ◽  
Unlabeled Data

scholarly journals Learning to Rank in Vector Spaces and Social Networks

2007 ◽  
Vol 4 (2-3) ◽  
pp. 267-298 ◽  
Author(s):  
Soumen Chakrabarti
Keyword(s):  
Social Networks ◽  
Learning To Rank ◽  
Vector Spaces
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