Relevant bibliographies by topics / Targeting advertisement machine learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Targeting advertisement machine learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Targeting advertisement machine learning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Targeting advertisement machine learning"

1

Ford, Elizabeth, Keegan Curlewis, Akkapon Wongkoblap, and Vasa Curcin. "Public Opinions on Using Social Media Content to Identify Users With Depression and Target Mental Health Care Advertising: Mixed Methods Survey." JMIR Mental Health 6, no. 11 (November 13, 2019): e12942. http://dx.doi.org/10.2196/12942.

Full text
Abstract:
Background Depression is a common disorder that still remains underdiagnosed and undertreated in the UK National Health Service. Charities and voluntary organizations offer mental health services, but they are still struggling to promote these services to the individuals who need them. By analyzing social media (SM) content using machine learning techniques, it may be possible to identify which SM users are currently experiencing low mood, thus enabling the targeted advertising of mental health services to the individuals who would benefit from them. Objective This study aimed to understand SM users’ opinions of analysis of SM content for depression and targeted advertising on SM for mental health services. Methods A Web-based, mixed methods, cross-sectional survey was administered to SM users aged 16 years or older within the United Kingdom. It asked participants about their demographics, their usage of SM, and their history of depression and presented structured and open-ended questions on views of SM content being analyzed for depression and views on receiving targeted advertising for mental health services. Results A total of 183 participants completed the survey, and 114 (62.3%) of them had previously experienced depression. Participants indicated that they posted less during low moods, and they believed that their SM content would not reflect their depression. They could see the possible benefits of identifying depression from SM content but did not believe that the risks to privacy outweighed these benefits. A majority of the participants would not provide consent for such analysis to be conducted on their data and considered it to be intrusive and exposing. Conclusions In a climate of distrust of SM platforms’ usage of personal data, participants in this survey did not perceive that the benefits of targeting advertisements for mental health services to individuals analyzed as having depression would outweigh the risks to privacy. Future work in this area should proceed with caution and should engage stakeholders at all stages to maximize the transparency and trustworthiness of such research endeavors.
APA, Harvard, Vancouver, ISO, and other styles
2

E, Prabhakar, Suresh Kumar V.S, Nandagopal S, and Dhivyaa C.R. "Mining Better Advertisement Tool for Government Schemes Using Machine Learning." International Journal of Psychosocial Rehabilitation 23, no. 4 (December 20, 2019): 1122–35. http://dx.doi.org/10.37200/ijpr/v23i4/pr190439.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Et. al., D. Ramya ,. "Click Prediction for Advertisement in Websites using Linear Regression." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 4 (April 11, 2021): 271–76. http://dx.doi.org/10.17762/turcomat.v12i4.504.

Full text
Abstract:
Prediction is one of the most powerful and effective method used nowadays for improvement in business. Machine Learning Algorithms plays a vital role in predicting the future of business. It is widely used in the field of Marketing and Advertising fields also. The Commercial Value for the advertisement is gained based on the user click on the website. Digital advertisement and marketing play very important role in influencing the profit of business. Many Machine Learning algorithms were used for predicting and analyzing the online advertisement. In this paper, Linear Regression is used for predicting the user click on the advertisement.
APA, Harvard, Vancouver, ISO, and other styles
4

Sun, Yan, Qian Wu, and Wendi Li. "A Push Model of Advertisement Classification Matching Based on Machine Learning." IOP Conference Series: Materials Science and Engineering 782 (April 15, 2020): 052050. http://dx.doi.org/10.1088/1757-899x/782/5/052050.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wilkinson, Matthew C., and Andrew J. Meade. "Neural-Network-Inspired Machine Learning for Autonomous Lunar Targeting." Journal of Aerospace Information Systems 11, no. 7 (July 2014): 458–66. http://dx.doi.org/10.2514/1.i010166.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Fan, Shijie, Yu Chen, Cheng Luo, and Fanwang Meng. "Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases." Current Pharmaceutical Design 24, no. 34 (January 22, 2019): 3998–4006. http://dx.doi.org/10.2174/1381612824666181112114228.

Full text
Abstract:
Background: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine. Methods: In this minireview, we will focus on reviewing the fundamentals and applications of machine learning methods which are regularly used in epigenetics filed and explain their features. Their advantages and disadvantages will also be discussed. Results: Machine learning algorithms have accelerated studies in precision medicine targeting epigenetics diseases. Conclusion: In order to make full use of machine learning algorithms, one should get familiar with the pros and cons of them, which will benefit from big data by choosing the most suitable method(s).
APA, Harvard, Vancouver, ISO, and other styles
7

Miao, Yuantian, Chao Chen, Lei Pan, Qing-Long Han, Jun Zhang, and Yang Xiang. "Machine Learning–based Cyber Attacks Targeting on Controlled Information." ACM Computing Surveys 54, no. 7 (July 2021): 1–36. http://dx.doi.org/10.1145/3465171.

Full text
Abstract:
Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years. Due to the booming development and deployment of advanced analytics solutions, novel stealing attacks utilize machine learning (ML) algorithms to achieve high success rate and cause a lot of damage. Detecting and defending against such attacks is challenging and urgent so governments, organizations, and individuals should attach great importance to the ML-based stealing attacks. This survey presents the recent advances in this new type of attack and corresponding countermeasures. The ML-based stealing attack is reviewed in perspectives of three categories of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information. Recent publications are summarized to generalize an overarching attack methodology and to derive the limitations and future directions of ML-based stealing attacks. Furthermore, countermeasures are proposed towards developing effective protections from three aspects—detection, disruption, and isolation.
APA, Harvard, Vancouver, ISO, and other styles
8

Zhang, Sen, Zheng Liu, and Wendong Xiao. "A Hierarchical Extreme Learning Machine Algorithm for Advertisement Click-Through Rate Prediction." IEEE Access 6 (2018): 50641–47. http://dx.doi.org/10.1109/access.2018.2868998.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Konapure, R. C., and L. M. R. J. Lobo. "Video Content-Based Advertisement Recommendation System using Classification Technique of Machine Learning." Journal of Physics: Conference Series 1854, no. 1 (April 1, 2021): 012025. http://dx.doi.org/10.1088/1742-6596/1854/1/012025.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Lotfi, Sogol, Ziyan Zhang, Gayatri Viswanathan, Kaitlyn Fortenberry, Aria Mansouri Tehrani, and Jakoah Brgoch. "Targeting Productive Composition Space through Machine-Learning-Directed Inorganic Synthesis." Matter 3, no. 1 (July 2020): 261–72. http://dx.doi.org/10.1016/j.matt.2020.05.002.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Targeting advertisement machine learning"

1

Poletti, Matteo. "Learning to target advertisements at Spotify." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170660.

Full text
Abstract:
The goal of this thesis work is to develop a methodology to optimize advertisement targeting inside the Spotify platform. By understanding the relevance of advertisements to users, the advertisement efficacy can be improved leading to an overall reduced advertisement load, to a better user experience, and to an increased advertisement revenue. Three types of advertisement are delivered inside the Spotify platform: house advertisements, which promote the Spotify premium subscription, label advertisements, which promote artists and albums and commercial advertisements, which promote external brands or products. For each type, machine learning models were implemented to optimize the matching of advertisements to users. The house advertisement targeting focused on maximizing the conversions of users from free to premium subscriptions. Unlike traditional advertisement targeting models, which focus on estimating the user probability of converting, the proposed approach focuses on estimating the change in probability of converting when an advertisement is delivered, so that only impressions producing a true uplift are delivered. The targeting of label advertisements optimized the number of users starting to listen to the advertised music. The implemented approach delivers relevant advertisements based on an evaluation of the affinity between the user's music and the advertised music and on the user's listening behavior. The commercial advertisement targeting optimized the number of clicks on advertisements. Given that commercial advertisements advertise external products, the main challenge faced was the lack of relevant data to inform the targeting. The implemented approach tries to deal with this problem by combining feature based methods with collaborative filtering methods. The main contribution of this thesis work is the implementation of machine learning models to improve advertisement targeting inside the Spotify platform. In particular, the proposed methodology uses uplift modeling, with a modified approach to handle bias in the training data, and also makes use of meta-data to better understand the context of the campaigns.
APA, Harvard, Vancouver, ISO, and other styles
2

Hedlund, Richard. "Predicting Visual Fixation on Digital Advertisement using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272115.

Full text
Abstract:
Despite the fact that ad-tech being a multi-billion dollar industry, the percentage of digital ads which are actually being clicked on is as low as 0.1 % in many cases. The performance of ads which can be clicked on are often measured by click-through rate (CTR), in other words, action based. However, for ads in which the advertiser’s goal is to only evoke brand/product awareness, they have to rely on metrics such as impressions or in-views. This master’s thesis aims to investigate if advertisement fixation can be used as a complementary metric to CTR. This was formulated by two research questions. The first one focused on to what extent machine learning models can predict advertisement fixation using web browsing data including eye tracking observations from a passive panel in Sweden, whereas the second research question focused on to give evidence into the most prominent features when predicting advertisement fixation. Predictive performance of four popular machine learning models, previously used in CTR prediction; Logistic Regression, Random Forest, XGBoost, and Field-Aware Factorization Machines were analyzed. Logistic Regression and Random Forest along with k-fold cross validation were used to validate the process of incremental feature engineering. The results demonstrated that an ensemble of three of the models could predict advertisement fixation with an F1-score of 0.5972, and an AUC-ROC value of 0.8005, where the latter is comparable to previous research in CTR prediction. In addition, the most prominent features when predicting advertisement fixation were concluded to be hostname, brand, ad type, x-coordinate, as well as the width and height of the ad. In conclusion, this shows that advertisement fixation can be predicted based on web browsing data. Further research is needed to determine if advertisement fixation should be used as a complement to CTR, and whether it will be adopted by the ad-tech industry.
Trots att ad-tech industrin genererar intäkter motsvarande miljardbelopp så kan andelen digitala annonser som klickas på vara så låg som 0.1 % i många fall. Prestandan av klickbara annonser mäts ofta genom klickfrekvens (CTR) vilka är handlingsbaserade. Däremot, annonser vars annonsör har som mål att väcka medvetenhet för varumärket eller produkten, förlitar sig på andra typer av mätverktyg som intryck (eng: impressions) eller visningar på skärmen (eng: in-views). Målet med den här masteruppsatsen är att undersöka om visuell fixation av annonser kan agera som ett komplimenterande mätverktyg till CTR. Det här låg till grund för frågeställningarna som fokuserade på till vilken grad som maskininlärning kan förutspå fixation av annonser med hjälp av webbaserad data och observationer av ögonrörelser från en panel i Sverige, men också att ge insikt i vilka variabler som är mest framträdande till att förutspå fixation av annonser. Prestandan i att förutspå fixation av annonser från fyra populära maskininlärningsmodeller analyserades; logistisk regression, Random Forest, XGBoost och Field-Aware Factorization Machines, som tidigare har applicerats i att förutspå CTR. Logistisk regression och Random Forest användes med hjälp av k-faldig korsvalidering för att validera processen av att inkrementellt addera och transformera variabler. Resultatet visade att en kombination av tre av modellerna (eng: ensemble) kunde förutspå fixation av annonser med ett F1-värde av 0.5972 och ett AUC-ROC värde av 0.8005. Det senare värdet är jämförbart med tidigare prestanda i att förutspå CTR. Vidare var hemsida, varumärke, annonstyp, x-koordinat, samt bredd och höjd de mest framträdande variablerna för att förutspå fixation av annonser. Avslutningsvis visar det här att fixation av annonser kan förutspås baserat på webbaserad data från användare som surfar på internet. Framtida forskning får utvisa om fixation av annonser kommer att användas som ett komplimenterande mätverktyg till CTR, samt om det kommer att bli mottaget av ad-tech industrin.
APA, Harvard, Vancouver, ISO, and other styles
3

Timoshenko, Artem. "Machine learning methods for targeting and new product development." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123572.

Full text
Abstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references.
Chapter 1: Market research traditionally relies on interviews and focus groups to identify customer needs. User-generated content (UGC), such as online reviews, social media, and call-center data, provides an opportunity to identify customer needs more efficiently. Established methods are not well-suited for large UGC datasets because much of the content is uninformative or repetitive. We propose a machine learning approach for identifying customer needs from UGC and evaluate the method using a new dataset. Once identified, the needs can be used to inform marketing strategy, brand positioning and new product development. Chapter 2: Targeting policies are used in marketing to match different firm actions to different customers. For example, retailers want to send different promotions to different customers, real estate agents want to show different homes, and car dealers want to propose different prices.
We conduct two large-scale field experiments to evaluate seven methods widely used to design targeting policies. The findings compare the performance of the targeting methods and demonstrate how well the methods address common data challenges. The challenges we study are covariate shift, concept shift, information loss through aggregation, and imbalanced data. We show that model-driven methods perform better than distance-driven methods and classification methods when the training data is ideal. However, the performance advantage vanishes in the presence of the challenges that affect the quality of the training data. Chapter 3: Firms typically compare the performance of different targeting policies by implementing the champion versus challenger experimental design. These experiments randomly assign customers to receive marketing actions recommended by either the existing (champion) policy or the new (challenger) policy, and then compare the aggregate outcomes.
We recommend an alternative experimental design and propose an estimation approach to improve the evaluation of targeting policies. The recommended experimental design randomly assigns customers to marketing actions. This allows evaluation of any targeting policy without requiring an additional experiment, including policies designed after the experiment is implemented. The proposed estimation approach identifies customers for whom different policies recommend the same action and recognizes that for these customers there is no difference in performance. This allows for a more precise comparison of the policies. We illustrate the advantages of the experimental design and the estimation approach using data from an actual field experiment. We also demonstrate that the grouping of customers, which is the foundation of our estimation approach, can help to improve the training of new targeting policies.
Chapter 4: Coupon personalization requires to predict how different combinations of coupons affect customer purchasing behavior. We develop a nonparametric model which predicts product choice for the entire assortment of a large retailer. Our model is nonparametric and is based on a deep neural network. The model inputs purchasing histories of individual customers and the coupon assignments to predict individual purchasing decisions. The model operates without ex-ante definitions of product categories. We evaluate the proposed product choice model in simulations. Our model significantly outperforms the baseline machine learning methods in terms of the prediction accuracy. Coupon personalization based on our model also achieves a substantially higher revenue compared to the baseline prediction methods.
by Artem Timoshenko.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Sloan School of Management
APA, Harvard, Vancouver, ISO, and other styles
4

Haupt, Johannes Sebastian. "Machine Learning for Marketing Decision Support." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21554.

Full text
Abstract:
Die Digitalisierung der Wirtschaft macht das Customer Targeting zu einer wichtigen Schnittmenge von Marketing und Wirtschaftsinformatik. Marketingtreibende können auf Basis von soziodemografischen und Verhaltensdaten gezielt einzelne Kunden mit personalisierten Botschaften ansprechen. Diese Arbeit erweitert die Perspektive der Forschung im Bereich der modellbasierten Vorhersage von Kundenverhalten durch 1) die Entwicklung und Validierung neuer Methoden des maschinellen Lernens, die explizit darauf ausgelegt sind, die Profitabilität des Customer Targeting im Direktmarketing und im Kundenbindungsmanagement zu optimieren, und 2) die Untersuchung der Datenerfassung mit Ziel des Customer Targeting aus Unternehmens- und Kundensicht. Die Arbeit entwickelt Methoden welche den vollen Umfang von E-Commerce-Daten nutzbar machen und die Rahmenbedingungen der Marketingentscheidung während der Modellbildung berücksichtigen. Die zugrundeliegenden Modelle des maschinellen Lernens skalieren auf hochdimensionale Kundendaten und ermöglichen die Anwendung in der Praxis. Die vorgeschlagenen Methoden basieren zudem auf dem Verständnis des Customer Targeting als einem Problem der Identifikation von Kausalzusammenhängen. Die Modellschätzung sind für die Umsetzung profitoptimierter Zielkampagnen unter komplexen Kostenstrukturen ausgelegt. Die Arbeit adressiert weiterhin die Quantifizierung des Einsparpotenzials effizienter Versuchsplanung bei der Datensammlung und der monetären Kosten der Umsetzung des Prinzips der Datensparsamkeit. Eine Analyse der Datensammlungspraktiken im E-Mail-Direktmarketing zeigt zudem, dass eine Überwachung des Leseverhaltens in der Marketingkommunikation von E-Commerce-Unternehmen ohne explizite Kundenzustimmung weit verbreitet ist. Diese Erkenntnis bildet die Grundlage für ein auf maschinellem Lernen basierendes System zur Erkennung und Löschung von Tracking-Elementen in E-Mails.
The digitization of the economy has fundamentally changed the way in which companies interact with customers and made customer targeting a key intersection of marketing and information systems. Building models of customer behavior at scale requires development of tools at the intersection of data management and statistical knowledge discovery. This dissertation widens the scope of research on predictive modeling by focusing on the intersections of model building with data collection and decision support. Its goals are 1) to develop and validate new machine learning methods explicitly designed to optimize customer targeting decisions in direct marketing and customer retention management and 2) to study the implications of data collection for customer targeting from the perspective of the company and its customers. First, the thesis proposes methods that utilize the richness of e-commerce data, reduce the cost of data collection through efficient experiment design and address the targeting decision setting during model building. The underlying state-of-the-art machine learning models scale to high-dimensional customer data and can be conveniently applied by practitioners. These models further address the problem of causal inference that arises when the causal attribution of customer behavior to a marketing incentive is difficult. Marketers can directly apply the model estimates to identify profitable targeting policies under complex cost structures. Second, the thesis quantifies the savings potential of efficient experiment design and the monetary cost of an internal principle of data privacy. An analysis of data collection practices in direct marketing emails reveals the ubiquity of tracking mechanisms without user consent in e-commerce communication. These results form the basis for a machine-learning-based system for the detection and deletion of tracking elements from emails.
APA, Harvard, Vancouver, ISO, and other styles
5

MELLO, SIMON. "VATS : Voice-Activated Targeting System." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279837.

Full text
Abstract:
Machine learning implementations in computer vision and speech recognition are wide and growing; both low- and high-level applications being required. This paper takes a look at the former and if basic implementations are good enough for real-world applications. To demonstrate this, a simple artificial neural network coded in Python and already existing libraries for Python are used to control a laser pointer via a servomotor and an Arduino, to create a voice-activated targeting system. The neural network trained on MNIST data consistently achieves an accuracy of 0.95 ± 0.01 when classifying MNIST test data, but also classifies captured images correctly if noise-levels are low. This also applies to the speech recognition, rarely giving wrong readings. The final prototype achieves success in all domains except turning the correctly classified images into targets that the Arduino can read and aim at, failing to merge the computer vision and speech recognition.
Maskininlärning är viktigt inom röstigenkänning och datorseende, för både små såväl som stora applikationer. Syftet med det här projektet är att titta på om enkla implementationer av maskininlärning duger för den verkligen världen. Ett enkelt artificiellt neuronnät kodat i Python, samt existerande programbibliotek för Python, används för att kontrollera en laserpekare via en servomotor och en Arduino, för att skapa ett röstaktiverat identifieringssystem. Neuronnätet tränat på MNIST data når en precision på 0.95 ± 0.01 när den försöker klassificera MNIST test data, men lyckas även klassificera inspelade bilder korrekt om störningen är låg. Detta gäller även för röstigenkänningen, då den sällan ger fel avläsningar. Den slutliga prototypen lyckas i alla domäner förutom att förvandla bilder som klassificerats korrekt till mål som Arduinon kan läsa av och sikta på, vilket betyder att prototypen inte lyckas sammanfoga röstigenkänningen och datorseendet.
APA, Harvard, Vancouver, ISO, and other styles
6

Braman, Nathaniel. "Novel Radiomics and Deep Learning Approaches Targeting the Tumor Environment to Predict Response to Chemotherapy." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1586546527544791.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Cockroft, Nicholas T. "Applications of Cheminformatics for the Analysis of Proteolysis Targeting Chimeras and the Development of Natural Product Computational Target Fishing Models." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156596730476322.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Chang, Jeremy, and 張書瑀. "Machine Learning Application: Correlation between Visual and Video Advertisement." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/bwfdpb.

Full text
Abstract:
碩士
東海大學
資訊工程學系
107
Thanks to the popularity of modern networks, the number of Internet users has been growing high. More people watch videos on the Internet than that on televisions. Advertising industry on the Internet experience a vigorous growth in recent years. At the same time, people come to perceive different opinions and thoughts on the efficiency of video advertisements. 　　Online advertising is usually based on “CTR”, an abbreviation of “Click through Rate”, to measure the efficiency of the advertisements or the total length of footage that advertisements showed to the viewers and the number of times each advertisement is actually clicked by the viewers. However, these evaluation methods are generally considered only reference-worthy. There is a more promising way to confirm the advertisements are actually watched by people. It is eye tracking technology. 　　Our research discusses the application of eye tracking device, through the visual analysis system we developed, to collect the information from viewers’ sight line, which directly allows us to recognize in which objects or details that video viewers are interested and to further acknowledge the efficiency of video advertisements. Furthermore, we also employ the technology of Artificial Intelligence - Machine Learning in the system, so the machine can analyze the final result and to further predict more information from data we have collected in the advertisements.
APA, Harvard, Vancouver, ISO, and other styles
9

Hu, Fei, and 胡斐. "Research on CTR Prediction for Advertisement based on Machine Learning of Distributed Platform." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/8eark8.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Targeting advertisement machine learning"

1

McBride, Linden, and Austin Nichols. Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning. World Bank, Washington, DC, 2016. http://dx.doi.org/10.1596/1813-9450-7849.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Barker, Richard. Achieving future impact. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198737780.003.0007.

Full text
Abstract:
To propel change forward we need not just a good sense of direction but also a sense of the prize, for patients and the health system, if we are successful. A wide range of new technologies, from technologies now coming into our hands, from gene editing to machine learning, have the potential to empower precision medicine to overcome some of mankind’s most intractable challenges: cancer, inherited diseases, aging, dementia—among many others. Taken together, the changes we propose to the innovation process could bring at least an order of magnitude greater net patient benefit over the lifetime of products, as a result of faster development, better targeting, more consistent reimbursement, swifter adoption, and better utilization.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Targeting advertisement machine learning"

1

Koutsopoulos, Iordanis, and Panagiotis Spentzouris. "Native Advertisement Selection and Allocation in Social Media Post Feeds." In Machine Learning and Knowledge Discovery in Databases, 588–603. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46128-1_37.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Xia, Yuan, Jingbo Zhou, Jingjia Cao, Yanyan Li, Fei Gao, Kun Liu, Haishan Wu, and Hui Xiong. "Intent-Aware Audience Targeting for Ride-Hailing Service." In Machine Learning and Knowledge Discovery in Databases, 136–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10997-4_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Raj, Jaladi Guna Vardhan Amrutha, Jagannath Patro Allupati, and G. Kalaiarasi. "Identifying and Detection of Advertisement Click Fraud Based on Machine Learning." In Lecture Notes in Electrical Engineering, 525–34. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8752-8_53.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Chaudhuri, Sougata, Georgios Theocharous, and Mohammad Ghavamzadeh. "Importance of Recommendation Policy Space in Addressing Click Sparsity in Personalized Advertisement Display." In Machine Learning and Data Mining in Pattern Recognition, 32–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62416-7_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Rawat, Sumanu, Aman Chopra, Siddhartha Singh, and Shobhit Sinha. "Mid Roll Advertisement Placement Using Multi Modal Emotion Analysis." In Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series, 159–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30490-4_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

El-Shal, Ibrahim H., Mustafa A. Elattar, and Wael Badawy. "On the Application of Real-Time Deep Neural Network for Automatic License Plate Reading from Sequence of Images Targeting Edge Artificial Intelligence Architectures." In Enabling Machine Learning Applications in Data Science, 299–311. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6129-4_21.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Maffezzoli, A., and E. Wanke. "Explorative Data Analysis of In-Vitro Neuronal Network Behavior Based on an Unsupervised Learning Approach." In Machine Learning, 2068–80. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch812.

Full text
Abstract:
In the present chapter authors want to expose new insights in the field of Computational Neuroscience at regard to the study of neuronal networks grown in vitro. Such kind of analyses can exploit the availability of a huge amount of data thanks to the use of Multi Electrode Arrays (MEA), a multi-channel technology which allows capturing the activity of several different neuronal cells for long time recordings. Given the possibility of simultaneous targeting of various sites, neuroscientists are so applying such recent technology for various researches. The chapter begins by giving a brief presentation of MEA technology and of the data produced in output, punctuating some of the pros and cons of MEA recordings. Then we present an overview of the analytical techniques applied in order to extrapolate the hidden information from available data. Then we shall explain the approach we developed and applied on MEAs prepared in our cell culture laboratory, consisting of statistical methods capturing the main features of the spiking, in particular bursting, activity of various neuron, and performing data dimensionality reduction and clustering, in order to classify neurons according to their spiking properties having showed correlated features. Finally the chapter wants to furnish to neuroscientists an overview about the quantitative analysis of in-vitro spiking activity data recorded via MEA technology and to give an example of explorative analysis applied on MEA data. Such study is based on methods from Statistics and Machine Learning or Computer Science but at the same time strictly related to neurophysiological interpretations of the putative pharmacological manipulation of synaptic connections and mode of firing, with the final aim to extract new information and knowledge about neuronal networks behavior and organization.
APA, Harvard, Vancouver, ISO, and other styles
8

"1. Applying big data analytics to psychometric micro-targeting." In Machine Learning for Big Data Analysis, 1–30. De Gruyter, 2018. http://dx.doi.org/10.1515/9783110551433-001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

El Morr, Christo. "Virtual Communities, Machine Learning and IoT." In Research Anthology on Mental Health Stigma, Education, and Treatment, 381–89. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8544-3.ch022.

Full text
Abstract:
Mental health challenges such as stress, anxiety and depression are on the rise worldwide. Health virtual communities (VCs) is a rising paradigm that has proven to be efficient and effective in delivering mental health interventions that address self-management, diagnosis and treatment targeting people facing mental health challenges. However, current Health VCs have limited application; they lack the ability to provide access to coordinated services and to continuously collect and integrate data originating from different devices in a streamlined manner. The Internet of Things (IoT) and machine learning represent a unique opportunity to expand the Health Virtual Community applications in the mental health domain; however, they represent a unique situation where challenges arise. This article will discuss the opportunities and challenges that virtual communities, machine learning and IoT represent for mental health research.
APA, Harvard, Vancouver, ISO, and other styles
10

Vora, Deepali R., and Kamatchi R. Iyer. "Deep Learning in Engineering Education." In Machine Learning and Deep Learning in Real-Time Applications, 187–218. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3095-5.ch009.

Full text
Abstract:
The goodness measure of any institute lies in minimising the dropouts and targeting good placements. So, predicting students' performance is very interesting and an important task for educational information systems. Machine learning and deep learning are the emerging areas that truly entice more research practices. This research focuses on applying the deep learning methods to educational data for classification and prediction. The educational data of students from engineering domain with cognitive and non-cognitive parameters is considered. The hybrid model with support vector machine (SVM) and deep belief network (DBN) is devised. The SVM predicts class labels from preprocessed data. These class labels and actual class labels acts as input to the DBN to perform final classification. The hybrid model is further optimised using cuckoo search with Levy flight. The results clearly show that the proposed model SVM-LCDBN gives better performance as compared to simple hybrid model and hybrid model with traditional cuckoo search.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Targeting advertisement machine learning"

1

Seker, Sadi Evren. "Real Life Machine Learning Case on Mobile Advertisement: A Set of Real-Life Machine Learning Problems and Solutions for Mobile Advertisement." In 2016 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2016. http://dx.doi.org/10.1109/csci.2016.0104.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Yang, Fan, Bin An, and Xizhao Wang. "Co-clustering for queries and corresponding advertisement." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212131.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Chakiat, Abhijith, Nishant Oli, and Varun Kumar Modi. "Deduplication of Advertisement Assets Using Deep Learning Ensembles." In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00145.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Liu, Huxiao, Lianhai Wang, Weinan Zhang, and Wei Wang. "An Illegal Billboard Advertisement Detection Framework Based on Machine Learning." In the 2nd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3358528.3358549.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Sartor, Anderson Luiz, Pedro Henrique Exenberger Becker, Stephan Wong, Radu Marculescu, and Antonio Carlos Schneider Beck. "Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability." In 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). IEEE, 2019. http://dx.doi.org/10.1109/isvlsi.2019.00037.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Zhou, Datong, Maximilian Balandat, and Claire Tomlin. "Residential demand response targeting using machine learning with observational data." In 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016. http://dx.doi.org/10.1109/cdc.2016.7799295.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Lin, Tong, Laura Cervino, Xiaoli Tang, Nuno Vasconcelos, and Steve B. Jiang. "Tumor Targeting for Lung Cancer Radiotherapy Using Machine Learning Techniques." In 2008 Seventh International Conference on Machine Learning and Applications. IEEE, 2008. http://dx.doi.org/10.1109/icmla.2008.143.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Rathor, Abhinav, and Manasi Gyanchandani. "A review at Machine Learning algorithms targeting big data challenges." In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 2017. http://dx.doi.org/10.1109/iceeccot.2017.8284604.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Aiken, Emily L., Guadalupe Bedoya, Aidan Coville, and Joshua E. Blumenstock. "Targeting Development Aid with Machine Learning and Mobile Phone Data." In COMPASS '20: ACM SIGCAS Conference on Computing and Sustainable Societies. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3378393.3402274.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Woo, Yeongju, Pizarroso Troncoso Carlos Andres, Hieyong Jeong, and Choonsung Shin. "Classification of diabetic walking through machine learning: Survey targeting senior citizens." In 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2021. http://dx.doi.org/10.1109/icaiic51459.2021.9415250.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Targeting advertisement machine learning"

1

Aiken, Emily, Suzanne Bellue, Dean Karlan, Christopher Udry, and Joshua Blumenstock. Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance. Cambridge, MA: National Bureau of Economic Research, July 2021. http://dx.doi.org/10.3386/w29070.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Targeting advertisement machine learning' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography