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Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising [article]

Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, Quan Lu
2018 arXiv   pre-print
Click-through rate (CTR) prediction is a critical task in online display advertising.  ...  In this paper, we propose Field-weighted Factorization Machines (FwFMs) to model the different feature interactions between different fields in a much more memory-efficient way.  ...  CONCLUSION In this paper, we propose Field-weighted Factorization Machines (FwFMs) for CTR prediction in online display advertising.  ... 
arXiv:1806.03514v1 fatcat:ng5xyzq4hjeffnug6eb4icu67y

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  ...  What are the parties involved in the online digital advertising eco-systems? What type of data are available for user response prediction?  ...  The baseline factorization machine methods usually consider all combinations of feature values in different fields with the same weight.  ... 
arXiv:2101.02342v2 fatcat:clgefamcd5fmbeg5ephizy3zqu

Research on CTR Prediction Based on Deep Learning

Qianqian Wang, Fang'ai Liu, Shuning Xing, Xiaohui Zhao, Tianlai Li
2019 IEEE Access  
Click-through rate (CTR) prediction is critical in Internet advertising and affects web publisher's profits and advertiser's payment.  ...  INDEX TERMS Click through rate, deep learning, factorization machines, sponsored search, tensor decomposition.  ...  CONCLUSION In click-through rate prediction, the interaction between features is a key factor affecting the prediction rate.  ... 
doi:10.1109/access.2018.2885399 fatcat:qrnbjt46bjbobabpb33j62d34e

Field-aware Factorization Machines in a Real-world Online Advertising System [article]

Yuchin Juan, Damien Lefortier, Olivier Chapelle
2017 arXiv   pre-print
This paper presents some results from implementing this method in a production system that predicts click-through and conversion rates for display advertising and shows that this method it is not only  ...  Field-aware Factorization Machines (FFM) have recently been established as a state-of-the-art method for that problem and in particular won two Kaggle challenges.  ...  a production system that predicts click-through and conversion rates on display advertisements.  ... 
arXiv:1701.04099v3 fatcat:recjn5pp4zerbn6fis7dnar7ma

Deep Learning Based Modeling in Computational Advertising: A Winning Formula

Mengmeng Chen, Luis Rabelo
2018 Industrial Engineering & Management  
Targeting the correct customers is critical to computational advertising. Once you find your potential customer, you want to know who can offer the most suitable offer at the right time.  ...  Click through rate prediction A lot of different models was proposed to predict the click through rate in both academia and industry, which most of those are using machine learning.  ...  And we will apply the Character level CNN in click-through rate prediction.  ... 
doi:10.4172/2169-0316.1000266 fatcat:kaaqruzk4ffmpgmox6yacqobzi

Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network

Danqing Zhu, Syed Hassan Ahmed
2021 Computational Intelligence and Neuroscience  
In the era of big data information, how to effectively predict and analyze the click-through rate of information advertising is the key for enterprises in various fields to seek returns.  ...  This paper analyzes the characteristics of traditional prediction methods and the corresponding solutions and carries out feature learning and prediction model construction for advertising click-through  ...  At present, there are many research methods for advertising click-through rate prediction, such as predicting advertising click-through rate according to neural network algorithm and logical regression  ... 
doi:10.1155/2021/3484104 fatcat:de33jd5gjjbzhdrwdr5xby5avm

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.  ...  The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in Internet advertising.  ...  At present, the prediction of click-through rate for online advertising has attracted widespread attention from researchers in industry and academia.  ... 
doi:10.1155/2018/8056541 fatcat:hi25slmrnnggxfkb6cqbsovtpi

A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets

Jie-Hao Chen, Zi-Qian Zhao, Ji-Yun Shi, Chong Zhao
2017 Computational Intelligence and Neuroscience  
In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of  ...  computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences.  ...  As a result, Click-Through Rate (CTR) estimation is the critical factor in this three-side game and has thus become a hot research direction in the field of computational advertising.  ... 
doi:10.1155/2017/7259762 pmid:29209363 pmcid:PMC5676483 fatcat:ydvimxliwrdpnkd24qqh6fcefe

sDeepFM: Multi-Scale Stacking Feature Interactions for Click-Through Rate Prediction

Baohua Qiang, Yongquan Lu, Minghao Yang, Xianjun Chen, Jinlong Chen, Yawei Cao
2020 Electronics  
For estimating the click-through rate of advertisements, there are some problems in that the features cannot be automatically constructed, or the features built are relatively simple, or the high-order  ...  Furthermore, by learning the parameters through factorization, the structure can ensure high-order features being effectively learned in sparse data.  ...  Finally, the prediction result is the predicted click-through rate of an advertisement.  ... 
doi:10.3390/electronics9020350 fatcat:wakkph2uhjbdfj7fnpzrlxl4my

CTR Prediction Models Considering the Dynamics of User Interest

Hailong Zhang, Jinyao Yan, Yuan Zhang
2020 IEEE Access  
Click-through rate (CTR) prediction is one of the key areas in industrial bidding advertising.  ...  INDEX TERMS Click-through rate prediction, user interest, attention mechanism, gated recurrent unit.  ...  Therefore, predicting performance indicators such as click-through rate (CTR) is an important research topic in DSP [7] .  ... 
doi:10.1109/access.2020.2988115 fatcat:jn5a6tpkpjba3haxj345l2gk2y

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  
., Alibaba has nearly 12 hundred million customers in China. Click-Through Rate (CTR) forecasting is a primary task in the e-commerce advertisement system.  ...  This research proposes a hybrid model combining the Deep Interest Network (DIN) and eXtreme Deep Factorization Machine (xDeepFM) to perform CTR prediction robustly.  ...  Data introduction Alibaba provides the dataset from the Kaggle website, which indicates the click rate prediction regarding displayed Ads. In Table 1 , we show components of Taobao's dataset.  ... 
doi:10.7717/peerj-cs.716 pmid:34616892 pmcid:PMC8459778 fatcat:2txdlajxejdq3iwbn5flpgp2he

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  
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.  ...  Factorization machine provides second-order feature interactions by linearly multiplying hidden feature factors. However, real-world data present a complex and nonlinear structure.  ...  The statistics of the datasets are summarized in Table 2 . Criteo: Criteo is a benchmark dataset for CTR prediction; it contains 45 million users' click records for displayed ads.  ... 
doi:10.26599/tst.2020.9010053 fatcat:wtzk6gml4ng37fqto5iimdwd7u

Personalized Product Recommendation Method for Analyzing User Behavior Using DeepFM

Jianqiang Xu, Zhujiao Hu, Junzhong Zou
2021 Journal of Information Processing Systems  
Finally, based on the predicted click-through rate, products are recommended to users in sequence and fed back.  ...  Then, through the DeepFM parameter sharing strategy, the relationship between low-and high-order feature combinations is learned from log data, and the click rate prediction model is constructed.  ...  Also this work is supported by the project fund of Shanghai Economic and Information Commission and the application of artificial intelligence in new retail.  ... 
doi:10.3745/jips.01.0069 dblp:journals/jips/XuHZ21 fatcat:yxh2uu4vtjb7jdwyc32c5sf2d4

Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search [article]

Marcelo Tallis, Pranjul Yadav
2018 arXiv   pre-print
In online internet advertising, machine learning models are widely used to compute the likelihood of a user engaging with product related advertisements.  ...  However, the performance of traditional machine learning models is often impacted due to variations in user and advertiser behavior.  ...  [9] proposed Field aware Factorization Machines for classifying large sparse data.  ... 
arXiv:1806.08211v1 fatcat:f6wn4b2y7nbulbvsntavyesy6u

Field-aware Neural Factorization Machine for Click-Through Rate Prediction [article]

Li Zhang, Weichen Shen, Shijian Li, Gang Pan
2019 arXiv   pre-print
Feature engineering is very important to improve click-through rate prediction.  ...  We propose a mechannism named 'Field-aware Neural Factorization Machine' (FNFM).  ...  Field-aware Neural Factorization Machine We propose Field-aware Neural Factorization Machine (FNFM), a click-through prediction model which obtains the advantages of FFM in second order feature interaction  ... 
arXiv:1902.09096v1 fatcat:6ur3l25lmrakzof4f3zg7gl6z4
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