This document summarizes a real-world machine learning problem of selecting mobile advertisements to display to users. It describes the problem of choosing from a large pool of ads while dealing with imbalanced click-through data and temporal trends. The solution involved segmenting customers and ads, handling imbalanced data, and using time-series features to improve a ranking algorithm. The results were a 10% increase in clicks and conversions for the mobile operator. Future work may explore more multi-criteria decision making algorithms.
SUPPORT VECTOR MACHINE CLASSIFIER FOR SENTIMENT ANALYSIS OF FEEDBACK MARKETPL...AM Publications
Sentiment analysis is an interdisciplinary field between natural language processing, artificial intelligence and text mining. The main key of the sentiment analysis is the polarity that is meant by the sentiment is positive or negative (Chen, 2012). In this study using the method of classification support vector machine with the amount of data consumer reviews amounted to 648 data. The data obtained from consumer reviews from the marketplace with products sold is hand phone. The results of this study get 3 aspects that indicate sentiment analysis on the marketplace aspects of service, delivery and products. The slang dictionary used for the normation process is 552 words slang. This study compares the characteristic analysis to obtain the best classification result, because classification accuracy is influenced by characteristic analysis process. The result of comparison value from characteristic analysis between n-gram and TF-IDF by using Support Vector Machine method found that Unigram has the highest accuracy value, with accuracy value 80,87%. The results of this study explain that in the case of analysis sentiment at the aspect level with the comparison of characteristics with the classification model of support vector machine found that the analysis model of unigram character and classification of support vector machine is the best model
The disruptometer: an artificial intelligence algorithm for market insightsjournalBEEI
Social media data mining is rapidly developing to be a mainstream tool for marketing insights in today’s world, due to the abundance of data and often freely accessed information. In this paper, we propose a framework for market research purposes called the Disruptometer. The algorithm uses keywords to provide different types of market insights from data crawling. The preliminary algorithm data-mines information from Twitter and outputs 2 parameters-Product-to-Market Fit and Disruption Quotient, which is obtained from a brand’s customer value proposition, problem space, and incumbent space. The algorithm has been tested with a venture capitalist portfolio company and market research firm to show high correlated results. Out of 4 brand use cases, 3 obtained identical results with the
analysts ‘studies.
A study of change in advertising techniques in Satara TalukaOmkar Tembe
This presentation was prepared and presented by me during last year of my Post Graduation (M.B.A) in State Level Research Paper Presentation Competition named Avishkar and Anveshan, in which, I stood 1st in the primary round to become eligible in the state level round, where I ranked 2nd.
This presentation follows all the norms and rules regarding research, and is useful for the budding Researchers.
It was pure Advertisement related Research, having scope of Satara Taluka.
Its main focus was to study the changes in the advertisement techniques. This presentation is important for me, as it inspired me to become passionate about research.
Combining Behaviors and Demographics to Segment Online Audiences:Experiments ...Joni Salminen
Link to article: https://www.springerprofessional.de/en/combining-behaviors-and-demographics-to-segment-online-audiences/16204306
CITE: Jansen, Bernard J., Jung, S., Salminen, J., An, J. and Kwak, H. (2018), “Combining Behaviors and Demographics to Segment Online Audiences: Experiments with a YouTube Channel”, Proceedings of the 5th International Conference of Internet Science (INSCI 2018), Springer, St. Petersburg, Russia.
Link to Automatic Persona Generation: https://persona.qcri.org
SUPPORT VECTOR MACHINE CLASSIFIER FOR SENTIMENT ANALYSIS OF FEEDBACK MARKETPL...AM Publications
Sentiment analysis is an interdisciplinary field between natural language processing, artificial intelligence and text mining. The main key of the sentiment analysis is the polarity that is meant by the sentiment is positive or negative (Chen, 2012). In this study using the method of classification support vector machine with the amount of data consumer reviews amounted to 648 data. The data obtained from consumer reviews from the marketplace with products sold is hand phone. The results of this study get 3 aspects that indicate sentiment analysis on the marketplace aspects of service, delivery and products. The slang dictionary used for the normation process is 552 words slang. This study compares the characteristic analysis to obtain the best classification result, because classification accuracy is influenced by characteristic analysis process. The result of comparison value from characteristic analysis between n-gram and TF-IDF by using Support Vector Machine method found that Unigram has the highest accuracy value, with accuracy value 80,87%. The results of this study explain that in the case of analysis sentiment at the aspect level with the comparison of characteristics with the classification model of support vector machine found that the analysis model of unigram character and classification of support vector machine is the best model
The disruptometer: an artificial intelligence algorithm for market insightsjournalBEEI
Social media data mining is rapidly developing to be a mainstream tool for marketing insights in today’s world, due to the abundance of data and often freely accessed information. In this paper, we propose a framework for market research purposes called the Disruptometer. The algorithm uses keywords to provide different types of market insights from data crawling. The preliminary algorithm data-mines information from Twitter and outputs 2 parameters-Product-to-Market Fit and Disruption Quotient, which is obtained from a brand’s customer value proposition, problem space, and incumbent space. The algorithm has been tested with a venture capitalist portfolio company and market research firm to show high correlated results. Out of 4 brand use cases, 3 obtained identical results with the
analysts ‘studies.
A study of change in advertising techniques in Satara TalukaOmkar Tembe
This presentation was prepared and presented by me during last year of my Post Graduation (M.B.A) in State Level Research Paper Presentation Competition named Avishkar and Anveshan, in which, I stood 1st in the primary round to become eligible in the state level round, where I ranked 2nd.
This presentation follows all the norms and rules regarding research, and is useful for the budding Researchers.
It was pure Advertisement related Research, having scope of Satara Taluka.
Its main focus was to study the changes in the advertisement techniques. This presentation is important for me, as it inspired me to become passionate about research.
Combining Behaviors and Demographics to Segment Online Audiences:Experiments ...Joni Salminen
Link to article: https://www.springerprofessional.de/en/combining-behaviors-and-demographics-to-segment-online-audiences/16204306
CITE: Jansen, Bernard J., Jung, S., Salminen, J., An, J. and Kwak, H. (2018), “Combining Behaviors and Demographics to Segment Online Audiences: Experiments with a YouTube Channel”, Proceedings of the 5th International Conference of Internet Science (INSCI 2018), Springer, St. Petersburg, Russia.
Link to Automatic Persona Generation: https://persona.qcri.org
This presentation was prepared and presented in Research Paper Presentation Competitions called Avishkar and Anveshan held at Shivaji University,Kolhapur. It got 2nd prize in District Level,1st prize in State Level and got selected for National Level.
It is based on basics and fundamentals of research. It will be helpful for everyone, who is interested in the field of research, as it follows standard format of Research Methodology.
UTILIZING IMBALANCED DATA AND CLASSIFICATION COST MATRIX TO PREDICT MOVIE PRE...ijaia
In this paper, we propose a movie genre recommendation system based on imbalanced survey data and unequal classification costs for small and medium-sized enterprises (SMEs) who need a data-based and analytical approach to stock favored movies and target marketing to young people. The dataset maintains a detailed personal profile as predictors including demographic, behavioral and preferences information for each user as well as imbalanced genre preferences. These predictors do not include movies’ information such as actors or directors. The paper applies Gentle boost, Adaboost and Bagged tree ensembles as well as SVM machine learning algorithms to learn classification from one thousand observations and predict movie genre preferences with adjusted classification costs. The proposed recommendation system also selects important predictors to avoid overfitting and to shorten training time. This paper compares the test error among the above-mentioned algorithms that are used to recommend different movie genres. The prediction power is also indicated in a comparison of precision and recall with other state-of-the-art recommendation systems. The proposed movie genre recommendation system solves problems such as small dataset, imbalanced response, and unequal classification costs.
This slide provides a quick overview of different aspects of marketing research. This ppt is expected to help researchers, faculties, and students to understand various aspects of Research and especially 'Marketing Research'.
Youtube link of the video in ppt: https://www.youtube.com/watch?v=Mm0g8mVHffE&feature=youtu.be
McKinsey Global Institute Big data The next frontier for innova.docxandreecapon
McKinsey Global Institute
Big data: The next frontier for innovation, competition, and productivity 27
2. Bigdatatechniquesand technologies
A wide variety of techniques and technologies has been developed and adapted to aggregate, manipulate, analyze, and visualize big data. These techniques and technologies draw from several fields including statistics, computer science, applied mathematics, and economics. This means that an organization that intends to derive value from big data has to adopt a flexible, multidisciplinary approach. Some techniques and technologies were developed in a world with access to far smaller volumes and variety in data, but have been successfully adapted so that they are applicable to very large sets of more diverse data. Others have been developed more recently, specifically to capture value from big data. Some were developed by academics and others by companies, especially those with online business models predicated on analyzing big data.
This report concentrates on documenting the potential value that leveraging big data can create. It is not a detailed instruction manual on how to capture value, a task that requires highly specific customization to an organization’s context, strategy, and capabilities. However, we wanted to note some of the main techniques and technologies that can be applied to harness big data to clarify the way some
of the levers for the use of big data that we describe might work. These are not comprehensive lists—the story of big data is still being written; new methods and tools continue to be developed to solve new problems. To help interested readers find a particular technique or technology easily, we have arranged these lists alphabetically. Where we have used bold typefaces, we are illustrating the multiple interconnections between techniques and technologies. We also provide a brief selection of illustrative examples of visualization, a key tool for understanding very large-scale data and complex analyses in order to make better decisions.
TECHNIQUES FOR ANALYZING BIG DATA
There are many techniques that draw on disciplines such as statistics and computer science (particularly machine learning) that can be used to analyze datasets. In this section, we provide a list of some categories of techniques applicable across a range of industries. This list is by no means exhaustive. Indeed, researchers continue to develop new techniques and improve on existing ones, particularly in response to the need
to analyze new combinations of data. We note that not all of these techniques strictly require the use of big data—some of them can be applied effectively to smaller datasets (e.g., A/B testing, regression analysis). However, all of the techniques we list here can be applied to big data and, in general, larger and more diverse datasets can be used to generate more numerous and insightful results than smaller, less diverse ones.
A/B testing. A technique in which a control group is compa ...
Presentation at Socialcom2014: Gauging Heterogeneity in Online Consumer Behav...Natalie de Vries
In this paper we explore and analyse the heterogeneity existent within a seemingly homogenous sample of online consumer behaviours in terms of their demographic profile. The data from a sample of 371 survey respondents is clustered using various distance functions and a clustering algorithm. In doing so, the respondents are clustered based on their response profiles to online behaviour questions rather than their demographic characteristics or brand preferences. Through our results we highlight that high levels of heterogeneity amongst consumers within the same cluster exists in terms of the ‘types’ of brand categories they engage with through social media. This finding has implications for marketing strategies and consumer behaviour analysis as it highlights the importance of investigating consumer’s behavioural profiles in the online brand setting. Our method also provides an empirical guide to examining respondents’ heterogeneity in terms of response profiles rather than ‘traditional’ segmentation strategies based on basic demographic information or brand categories.
This presentation was prepared and presented in Research Paper Presentation Competitions called Avishkar and Anveshan held at Shivaji University,Kolhapur. It got 2nd prize in District Level,1st prize in State Level and got selected for National Level.
It is based on basics and fundamentals of research. It will be helpful for everyone, who is interested in the field of research, as it follows standard format of Research Methodology.
UTILIZING IMBALANCED DATA AND CLASSIFICATION COST MATRIX TO PREDICT MOVIE PRE...ijaia
In this paper, we propose a movie genre recommendation system based on imbalanced survey data and unequal classification costs for small and medium-sized enterprises (SMEs) who need a data-based and analytical approach to stock favored movies and target marketing to young people. The dataset maintains a detailed personal profile as predictors including demographic, behavioral and preferences information for each user as well as imbalanced genre preferences. These predictors do not include movies’ information such as actors or directors. The paper applies Gentle boost, Adaboost and Bagged tree ensembles as well as SVM machine learning algorithms to learn classification from one thousand observations and predict movie genre preferences with adjusted classification costs. The proposed recommendation system also selects important predictors to avoid overfitting and to shorten training time. This paper compares the test error among the above-mentioned algorithms that are used to recommend different movie genres. The prediction power is also indicated in a comparison of precision and recall with other state-of-the-art recommendation systems. The proposed movie genre recommendation system solves problems such as small dataset, imbalanced response, and unequal classification costs.
This slide provides a quick overview of different aspects of marketing research. This ppt is expected to help researchers, faculties, and students to understand various aspects of Research and especially 'Marketing Research'.
Youtube link of the video in ppt: https://www.youtube.com/watch?v=Mm0g8mVHffE&feature=youtu.be
McKinsey Global Institute Big data The next frontier for innova.docxandreecapon
McKinsey Global Institute
Big data: The next frontier for innovation, competition, and productivity 27
2. Bigdatatechniquesand technologies
A wide variety of techniques and technologies has been developed and adapted to aggregate, manipulate, analyze, and visualize big data. These techniques and technologies draw from several fields including statistics, computer science, applied mathematics, and economics. This means that an organization that intends to derive value from big data has to adopt a flexible, multidisciplinary approach. Some techniques and technologies were developed in a world with access to far smaller volumes and variety in data, but have been successfully adapted so that they are applicable to very large sets of more diverse data. Others have been developed more recently, specifically to capture value from big data. Some were developed by academics and others by companies, especially those with online business models predicated on analyzing big data.
This report concentrates on documenting the potential value that leveraging big data can create. It is not a detailed instruction manual on how to capture value, a task that requires highly specific customization to an organization’s context, strategy, and capabilities. However, we wanted to note some of the main techniques and technologies that can be applied to harness big data to clarify the way some
of the levers for the use of big data that we describe might work. These are not comprehensive lists—the story of big data is still being written; new methods and tools continue to be developed to solve new problems. To help interested readers find a particular technique or technology easily, we have arranged these lists alphabetically. Where we have used bold typefaces, we are illustrating the multiple interconnections between techniques and technologies. We also provide a brief selection of illustrative examples of visualization, a key tool for understanding very large-scale data and complex analyses in order to make better decisions.
TECHNIQUES FOR ANALYZING BIG DATA
There are many techniques that draw on disciplines such as statistics and computer science (particularly machine learning) that can be used to analyze datasets. In this section, we provide a list of some categories of techniques applicable across a range of industries. This list is by no means exhaustive. Indeed, researchers continue to develop new techniques and improve on existing ones, particularly in response to the need
to analyze new combinations of data. We note that not all of these techniques strictly require the use of big data—some of them can be applied effectively to smaller datasets (e.g., A/B testing, regression analysis). However, all of the techniques we list here can be applied to big data and, in general, larger and more diverse datasets can be used to generate more numerous and insightful results than smaller, less diverse ones.
A/B testing. A technique in which a control group is compa ...
Presentation at Socialcom2014: Gauging Heterogeneity in Online Consumer Behav...Natalie de Vries
In this paper we explore and analyse the heterogeneity existent within a seemingly homogenous sample of online consumer behaviours in terms of their demographic profile. The data from a sample of 371 survey respondents is clustered using various distance functions and a clustering algorithm. In doing so, the respondents are clustered based on their response profiles to online behaviour questions rather than their demographic characteristics or brand preferences. Through our results we highlight that high levels of heterogeneity amongst consumers within the same cluster exists in terms of the ‘types’ of brand categories they engage with through social media. This finding has implications for marketing strategies and consumer behaviour analysis as it highlights the importance of investigating consumer’s behavioural profiles in the online brand setting. Our method also provides an empirical guide to examining respondents’ heterogeneity in terms of response profiles rather than ‘traditional’ segmentation strategies based on basic demographic information or brand categories.
Prognosis - An Approach to Predictive Analytics- Impetus White PaperImpetus Technologies
For Impetus’ White Papers archive, visit- http://www.impetus.com/whitepaper
The paper talks about implementation of Behavioral Targeting for the ad world. This is a statistical machine learning algorithm that helps select most relevant ads to be displayed to a web user based on their historical data.
Alto_MKT RES_initial student suTimestampGenderWhat is your ageHav.docxgreg1eden90113
Alto_MKT RES_initial student suTimestampGenderWhat is your age?Have you ever been in one of my classes before?Please list which class(es) and whether it was online or in person. Place a comma in between classes (e.g., "Principles of Marketing online, Social Media Marketing in person")What do you think "Marketing Research" means or includes?Have you ever developed an online survey?What program(s) have you used to create the survey?Please rate your knowledge of statistics (you can use your DSC classes as an indicator). Please rate your knowledge of EXCEL.What marketing concepts are you interested in? (check all that apply; type additional topics in "Other")How comfortable are you with the following statistical techniques [independent t-tests]How comfortable are you with the following statistical techniques [dependent t-tests]How comfortable are you with the following statistical techniques [correlations]How comfortable are you with the following statistical techniques [measures of central tendency (Mean, median, mode)]How comfortable are you with the following statistical techniques [frequency tables]How comfortable are you with the following statistical techniques [regression (predicting one thing from something else)]How comfortable are you with the following statistical techniques [measuring group differences]2018/08/20 11:16:53 AM ASTFemale22YesPrinciples of Marketing in person, Integrated Marketing Communications onlineI think marketing research is a method that marketers use to create links and develop an understanding between producers and consumers, and the different opportunities and problems with a market. Yessurvey monkey44gamification;social media;location based trends (like pokemon go);mobile commerce (buying via mobile apps);designing marketing plans/strategies;designing marketing communications (ads, videos, press releases, etc);sports/athletes;television and/or movies33333332018/08/20 12:40:53 PM ASTMale20NoIncludes more statistics and analytical means of acquiring informationNo44gamification;social media;designing marketing plans/strategies;sports/athletes;television and/or movies54454342018/08/20 5:01:46 PM ASTFemale24YesSocial Media Marketing in personcollecting and analyzing the current market to recognize market opportunities as well as efficiently target consumers.No33gamification;social media;mobile commerce (buying via mobile apps);designing marketing plans/strategies;designing marketing communications (ads, videos, press releases, etc);television and/or movies22233222018/08/21 4:27:16 PM ASTMale24NoGathering data to help market somethingNo33social media;location based trends (like pokemon go);mobile commerce (buying via mobile apps);designing marketing plans/strategies;designing marketing communications (ads, videos, press releases, etc);political campaign marketing43442432018/08/21 8:29:15 PM ASTMale46YesIntegrated Marketing Communications - onlineAt first my thoughts were about gathering information about your target.
Political polling season is kicking into high gear – and pollsters want to ensure they are getting the most accurate data possible. While much of traditional polling is done on the phone, it has proven that it is not as accurate as it once was. What can be done?
Check out the deck from our webinar, The New Polling Mix: Increasing Accuracy With Online Surveys, to learn how incorporating online surveys into your polling mix can increase your overall accuracy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Real Life Machine Learning Case on Mobile Advertisement
1. A S E T O F R E A L - L I F E M A C H I N E L E A R N I N G
P R O B L E M S A N D S O L U T I O N S F O R M O B I L E
A D V E R T I S E M E N T
S A D I E V R E N S E K E R
S L I D E S A R E A V A I L A B L E A T :
W W W . S A D I E V R E N S E K E R . C O M
Real Life Machine Learning Case
on Mobile Advertisement
2. Outline
Problem Definition
Details of Data
Methodology and Solution
Results Achieved
Conclusion and Future Directions
4. Mobile Advertisement and Problems
Mobile Marketing in Turkey,
3 Operators
73.2 Million active subscribers
Market Size: 1.4 Billion USD
Question:
Which ad from the
ad giver will be displayed
on the Content?
5. Problems and Data
Pool of Advertisements
Customer Profiling (missing info)
Click streams
Demography
Operator / Device info
Prediction in Real time
Data Splitting and Selection (Seasonality, Splitting
data (Train / Test))
Imbalanced Data
6. Temporal Data
In Real Time, No split point for train/test
In experiments you can split
7. Which Data to Use?
Statistics
until now
now
time
morning
8. Which Data to Use?
Statistics
until now
now
time
morning
Statistics
from
Yesterday
9. Which Data to Use?
Statistics
until now
now
time
morning
Statistics
from
Yesterday
Same
time Slot
from
Yesterday
10. Which Data to Use?
Statistics
until now
now
time
morning
Statistics
from
Yesterday
Same
time Slot
from
Yesterday
Statistics
from last
week
Same
time Slot
from last
week
11. Which Data to Use?
Statistics
until now
now
time
morning
Statistics
from
Yesterday
Same
time Slot
from
Yesterday
Statistics
from last
week
Same
time Slot
from last
week
Or Last Month?
Or Last Year?
12. Temporal Feature Selection
Hour of day
Day of week
Special days and events (football games, holidays)
Last n minutes (what is the optimal period of time in
Time Series analysis?)
14. Methodology
Feature Extraction
Customer Segmentation
Click Streams
User Agent
Geographical Information
Product/Advertisement Segmentation
Advertisement Network
Advertisement Look and Feel
Time Series Analysis
Time Based Training Data Decision
Algorithm Selection
Algorithm Optimization
16. Solution: Imbalanced Data Sets
Synthetic Data generation (SMOTE)
Anomaly detection / Outlier Detection
Resampling (Random Undersampling)
Penalizing the model
Purchase Not purchase
Actual
classPredicted
class
C1 ¬ C1
C1 True
Positives
(TP)
False
Negatives
(FN)
¬ C1 False
Positives
(FP)
True
Negatives
(TN)
17. Advertisement Segmentation
Predefined Segments and advertisements are
prepared for the given segment by experts
Matching Algorithms
Customer
Segment
Advertisement
Segment
Match
18. Advertisement Segmentation
Predefined Segments and advertisements are
prepared for the given segment by experts
Matching Algorithms
Customer
Segment
Advertisement
Segment
Match
Time
Click
Stream
19. Advertisement Segmentation
Predefined Segments and advertisements are
prepared for the given segment by experts
Matching Algorithms
Customer
Segment
Match
Time
Click
Stream
w1
w2 w3
Advertisement
Segment
Ad
click stream factor (γ), content relativeness of web page history item i (η),
time spent on web page (t), publisher relativeness (π), ads previously displayed (α).
20. Implementation and Environment
Rapid Miner for experiments
Weka + Java in production
Some Python, MSSQL Stored procedures and C#
modules for speed.
21. Results
Previously a ranking algorithm was implemented.
At the start of week they put 50 new advertisements and they
rank the algorithms with their success in daily basis.
About 10% increase in clicks and subscriptions (Click
rates: originally 5.2/1000 (reported quarterly), now
6.1/1000), (Subscription rates: originally 38.2% ,
now 45.2%)
22. Future Work
MCDM Algorithms
ANP [30], VIKOR [31,32], TOPSIS [33], SAW [34], AHP
[35,36], Decision-Making Trial and Evaluation Laboratory
(DEMATEL) [37], Preference Ranking Organisation Method
for Enrichment Evaluations (PROMETHEE) [38], Data
Envelopment Analysis (DEA) [39,40], ELECTRE [41–44].
Additionally, some new MCDM techniques developed in
recent years, these techniques are; generalized regression with
intensities of preference (GRIP) [45], Complex Proportional
Assessment Method (COPRAS) [46–48], ARAS [48–50],
MOORA [51], and MOORA plus the full multiplicative form
(MULTIMOORA) [52], Step-Wise Weight Assessment Ratio
Analysis (SWARA) [53], Weighted Aggregated Sum Product
Assessment (WASPAS) [54]
23. References
Teng-Kai Fan, Chia-Hui Chang , "Sentiment-oriented contextual advertising" Knowledge and Information Systems, June 2010, Volume 23, Issue 3, pp 321–344
Peng-Ting Chen, Hsin-Pei Hsieh , “Personalized mobile advertising: Its key attributes, trends, and social impact “,Technological Forecasting & Social Change,
79 (2012) 543–557
I.S. Chang, Y. Tsujimura, M. Gen, T. Tozawa, An efficient approach for large scale project planning based on fuzzy Delphi method, Fuzzy Sets. Syst. 76 (3)
(1995) 277–288.
Seker, S. E., “Computerized Argument Delphi Technique”, IEEE Access, 2015, v. 3, pp. 368 - 380
. David Jingjun Xu, Stephen Shaoyi Liao, Qiudan Lid, “Combining empirical experimentation and modeling techniques: A design research approach for
personalized mobile advertising applications ”, Decision Support Systems 44 (2008) 710–724
H. Wold, Introduction to the second generation of multivariate analysis, in: H. Wold (Ed.), Theoretical Empiricism, Paragon House, New York, 1989.
Abdi. H., & Williams, L.J. (2010). "Principal component analysis". Wiley Interdisciplinary Reviews: Computational Statistics. 2 (4): 433–459
Kai Li , Timon C. Du , “Building a targeted mobile advertising system for location-based services“, Decision Support Systems, v. 54, 2012, pp. 1-8
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26. Real Life Machine Learning Case on Mobile
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