Paper Info
Title
Robust transfer integrated locally kernel embedding for click-through rate prediction.
Abstract
With the rapid development of online advertising, the click-through rate (CTR) prediction plays an important role in improving the benefits of advertising and user experience. CTR is the most commonly used evaluation indicator of the effects of online advertising. At present, the keys including feature extraction and user click behavior modeling have been taken into consideration by many researchers to design methods for CTR prediction. However, the characteristics of high-dimensional data sparseness and imbalance in advertising data are not fully considered, which results in the insufficient utilization of advertising information. To alleviate the problems of data sparsity and imbalance, this paper proposes a robust integrated locally kernel embedding (RILKE) model to solve data sparseness and incorporate unsupervised transfer learning into RILKE to form an improved model, named robust transfer integrated locally kernel embedding (RTILKE). Through theoretical analysis and empirical experiments, RTILKE can efficiently solve the data-imbalance problem of CTR prediction in online advertising, which effectively improves the prediction performance of advertising responses.
Year
DOI
Venue
2019
10.1016/j.ins.2019.04.006
Information Sciences
Keywords
Field
DocType
Click-through rate,Online advertising,Transfer learning,Locally linear embedding,Unsupervised learning
Kernel (linear algebra),Click-through rate,User experience design,Embedding,Transfer of learning,Online advertising,Design methods,Feature extraction,Artificial intelligence,Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
491
0020-0255
2
PageRank 
References 
Authors
0.37
0
4
Name
Order
Citations
PageRank
Ying Xie14714.48
Dan Jiang2248.36
Xinmei Wang37617.74
Rongbin Xu43710.01