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Automatic Image Dataset Construction from Click-through Logs Using Deep Neural Network

Yalong Bai, Kuiyuan Yang, Wei Yu, Chang Xu, Wei-Ying Ma, Tiejun Zhao
2015 Proceedings of the 23rd ACM international conference on Multimedia - MM '15  
Specifically, word representation and image representation are learned in a deep neural network from large amount of click-through logs, and further used to define word-word similarity and image-word similarity  ...  In this paper, we propose a deep learning based method to construct large scale image dataset in an automatic way.  ...  To the best of our knowledge, we are the first to use deep neural network for fully-automatic image dataset construction by gaining generalization ability from large scale click-through logs.  ... 
doi:10.1145/2733373.2806243 dblp:conf/mm/BaiYYXMZ15 fatcat:4znttui4kjdihchxghbmw6b7ta

Feature Interaction based Neural Network for Click-Through Rate Prediction [article]

Dafang Zou and Leiming Zhang and Jiafa Mao and Weiguo Sheng
2020 arXiv   pre-print
Click-Through Rate (CTR) prediction is one of the most important and challenging in calculating advertisements and recommendation systems.  ...  This paper aims to fully utilize the information between features and improve the performance of deep neural networks in the CTR prediction task.  ...  In advertising systems, click-through rate (CTR) prediction is generally used as a measure of advertising items.  ... 
arXiv:2006.05312v1 fatcat:gskgtn3kifeoxoy56h7p4apxoq

Usability and Security Testing of Online Links: A Framework for Click-Through Rate Prediction Using Deep Learning

Robertas Damaševičius, Ligita Zailskaitė-Jakštė
2022 Electronics  
As a case study, we analyse a CTR prediction task, using deep learning methods (factorization machines) to predict online fraud through clickbait.  ...  Many internet applications, such as internet advertising and recommendation systems, rely on click-through rate (CTR) prediction to anticipate the possibility that a user would click on an ad or product  ...  Deep Neural Networks for CTR Prediction High-order feature interactions might be harder to grasp. Deep neural networks (DNNs) can learn these deep-feature connections.  ... 
doi:10.3390/electronics11030400 fatcat:nmgvjbvpnbgk5kebmvp4xubrce

CAN: Effective cross features by global attention mechanism and neural network for ad click prediction

Wenjie Cai, Yufeng Wang, Jianhua Ma, Qun Jin
2022 Tsinghua Science and Technology  
This drawback has been addressed using deep neural networks (DNNs), which enable high-order nonlinear feature interactions.  ...  Online advertising click-through rate (CTR) prediction is aimed at predicting the probability of a user clicking an ad, and it has undergone considerable development in recent years.  ...  Then, highorder nonlinear feature interactions are mined through the neural network layer.  ... 
doi:10.26599/tst.2020.9010053 fatcat:wtzk6gml4ng37fqto5iimdwd7u

Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction [article]

Chenglei Niu, Guojing Zhong, Ying Liu, Yandong Zhang, Yongsheng Sun, Ailong He, Zhaoji Chen
2019 arXiv   pre-print
It is a big challenge to get state-of-the-art result through training deep neural network and embedding together, which falls into local optimal or overfitting easily.  ...  label.The output of SSM are then used in the Wide&Deep for CTR prediction.Experiments on two public datasets as well as real Weibo production dataset with over 1 billion samples have demonstrated the  ...  Deep Component. The deep component is a feed-forward neural network consists of multi layers of units using categorical and numberic features.  ... 
arXiv:1812.01353v5 fatcat:qacujwogpzct7h3ifipvspynpu

Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challenges

Ayesha Rashid, Muhammad Shoaib Farooq, Adnan Abid, Tariq Umer, Ali Kashif Bashir, Yousaf Bin Zikria
2021 Complex & Intelligent Systems  
AbstractIntention mining is a promising research area of data mining that aims to determine end-users' intentions from their past activities stored in the logs, which note users' interaction with the system  ...  Similarly, six important types of data sets used for this purpose have also been discussed in this work.  ...  [112] have used deep neural network techniques to extract social media intention from social media logs.  ... 
doi:10.1007/s40747-021-00342-9 fatcat:ak3y4ao2sbffjd5b3rbttidvjy

Implementation of Short Video Click-Through Rate Estimation Model Based on Cross-Media Collaborative Filtering Neural Network

Ying Feng, Guisheng Zhao, Gengxin Sun
2022 Computational Intelligence and Neuroscience  
In this paper, we analyze the construction of cross-media collaborative filtering neural network model to design an in-depth model for fast video click-through rate projection based on cross-media collaborative  ...  filtering neural network.  ...  filtering neural network model based on Deep FM for fast video click-through rate estimation.  ... 
doi:10.1155/2022/4951912 pmid:35685157 pmcid:PMC9173947 fatcat:tq3tdoj5qzha3ltxvpaz7thge4

An adaptive hybrid XdeepFM based deep Interest network model for click-through rate prediction system

Qiao Lu, Silin Li, Tuo Yang, Chenheng Xu
2021 PeerJ Computer Science  
From the traditional Logistic Regression algorithm to the latest popular deep neural network methods that follow a similar embedding and MLP, several algorithms are used to predict CTR.  ...  Click-Through Rate (CTR) forecasting is a primary task in the e-commerce advertisement system.  ...  To learn high-order interactions, neural networks use a feed-forward neural network on the field embedding vector e.  ... 
doi:10.7717/peerj-cs.716 pmid:34616892 pmcid:PMC8459778 fatcat:2txdlajxejdq3iwbn5flpgp2he

Neural IR Meets Graph Embedding: A Ranking Model for Product Search [article]

Yuan Zhang, Dong Wang, Yan Zhang
2019 arXiv   pre-print
However, it is challenging to use graph-based features, though proved very useful in IR literature, in these neural approaches.  ...  The proposed approach can not only help to overcome the long-tail problem of click-through data, but also incorporate external heterogeneous information to improve search results.  ...  automatically extracted from click-through data by GEPS.  ... 
arXiv:1901.08286v1 fatcat:f37ls5e2wzbj7k34utr36j7jrm

A New Approach for Advertising CTR Prediction Based on Deep Neural Network via Attention Mechanism

Qianqian Wang, Fang'ai Liu, Shuning Xing, Xiaohui Zhao
2018 Computational and Mathematical Methods in Medicine  
Click-through rate prediction is critical in Internet advertising and affects web publisher's profits and advertiser's payment.  ...  Our method exploits dimension reduction based on decomposition, takes advantage of the attention mechanism in neural network modelling, and improves FM to make feature interactions contribute differently  ...  Click-through Rate Estimation Based on Deep Neural Network One of the necessary steps in the click rate prediction system is to mine features that are highly correlated with the estimated task.  ... 
doi:10.1155/2018/8056541 fatcat:hi25slmrnnggxfkb6cqbsovtpi

Convolutional Neural Network-Based Cross-Media Semantic Matching and User Adaptive Satisfaction Analysis Model

Lanlan Jiang, Gengxin Sun
2022 Computational Intelligence and Neuroscience  
Based on the existing convolutional neural network, this paper uses rich information.  ...  the feature information of the image and uses dilated instead of traditional convolution.  ...  In addition to the deep typical correlation analysis mentioned above, Li and Yuan proposed a deep semantic matching method using convolutional neural networks and fully connected networks to map images  ... 
doi:10.1155/2022/4244675 pmid:35535181 pmcid:PMC9078763 fatcat:5gdiv7u6rra6xjcmfe4jim77ne

GCN-int: A Click-Through Rate Prediction Model Based on Graph Convolutional Network Interaction

Yuchen Liu, Chuanzhen Li, Han Xiao, Juanjuan Cai
2021 IEEE Access  
Compared with Deep Neural Network, which is a blackbox structure, Graph Neural Network can be used to observe which features interact.  ...  Generally, DNN uses multiple hidden layers of the neural network for embedding learning, resulting in feature interactions within the neural network being learned automatically.  ... 
doi:10.1109/access.2021.3116705 fatcat:w4zaxx3juzbrxm7tpggnxrgfva

User Response Prediction in Online Advertising [article]

Zhabiz Gharibshah, Xingquan Zhu
2021 arXiv   pre-print
The prosperity of online campaigns is a challenge in online marketing and is usually evaluated by user response through different metrics, such as clicks on advertisement (ad) creatives, subscriptions  ...  to products, purchases of items, or explicit user feedback through online surveys.  ...  Comparing to deep neural network based methods, recurrent neural networks suffer from computational and storage overheads.  ... 
arXiv:2101.02342v2 fatcat:clgefamcd5fmbeg5ephizy3zqu

M2FN: Multi-step modality fusion for advertisement image assessment

Kyung-Wha Park, Jung-Woo Ha, JungHoon Lee, Sunyoung Kwon, Kyung-Min Kim, Byoung-Tak Zhang
2021 Applied Soft Computing  
Although recent studies have attempted to use deep neural networks for this purpose, these studies have not utilized image-related auxiliary attributes, which include embedded text frequently found in  ...  We verified M2FN on the AVA dataset, which is widely used for aesthetic image assessment, and then demonstrated that M2FN can achieve state-of-the-art performance in preference prediction using a real-world  ...  The authors thank NAVER AI LAB, NAVER CLOVA for constructive discussion and LINE Corp. for preparing data.  ... 
doi:10.1016/j.asoc.2021.107116 fatcat:lutrwy3ycvdrtfvw7mvca6ozcu

Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate [article]

Amit Livne, Roy Dor, Eyal Mazuz, Tamar Didi, Bracha Shapira, Lior Rokach
2020 arXiv   pre-print
The click-through rate (CTR) reflects the ratio of clicks on a specific item to its total number of views. It has significant impact on websites' advertising revenue.  ...  XDBoost is an iterative three-stage neural network model influenced by the traditional machine learning boosting mechanism.  ...  To address this limitation, we suggest a new iterative boosting deep neural network (DNN) algorithm, XDBoost, that automatically crafting artificial features using a limited amount of data.  ... 
arXiv:2007.13087v1 fatcat:aujqcpa4ofgsjduankwjwow5mu
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