Fairness in Search & RecSys 네이버 검색 콜로키움 김진영Jin Young Kim
검색 및 추천 시스템의 사회적 역할이 커지면서, 그 결과의 공정성 역시 최근 관심사로 대두되었다. 본 발표에서는 검색 및 추천시스템의 공정성 이슈 및 그 해법을 다룬다. 공정한 검색 및 추천 결과를 정의하는 다양한 방법, 공정성의 결여가 미치는 자원 배분 및 스테레오타이핑 문제, 그리고 검색 및 추천시스템 개발의 각 단계별로 어떤 해결책이 있는지를 최신 연구 중심으로 살펴본다. 마지막으로 실제 공정한 시스템 개발을 위한 실무적인 고려사항을 다룬다.
Measuring the Quality of Online Service - Jinyoung kimJin Young Kim
This document discusses methods for measuring the quality of online services. It describes how major companies like Google, Facebook, and Netflix collect data through user behavior, panel surveys, and direct user feedback at different stages of their services. Panel surveys can provide insights but have limitations, while user behavior data is abundant but noisy. The document also provides examples of how to design panel surveys and side-by-side evaluations to assess search engine result pages. It concludes that the best approach is to combine various data collection methods depending on the service characteristics and lifecycle.
Scribd, inc. slide share - google play - worldwide - 2021-05-03 18-07_11Noômen Ben Hassin
The document discusses how to use App Review Management to support mobile app growth. It notes that responding to reviews can increase customer satisfaction and retention, improve the product, and save time and money. It provides an overview of a mobile app's ratings and reviews and recommends automating responses to repetitive reviews, addressing unanswered reviews, and integrating helpdesk tools to streamline support and increase productivity. The goal is to increase organic growth, in-app transactions, and average lifetime customer value through higher ratings and improved customer experience.
Most of us use e-shopping (Any product) these days and refer its rating or reviews before we download or buy that product. Amazon/Play store provide a great number of products but unfortunately few of those product reviews are fraud. Hence such products must be marked, so that they will be recognizable for rest of the users. Here we are comparing reviews from two sites so that we can get more clear idea. We can get higher probability of getting real reviews if we take data from multiple sites. We are proposing a system to develop an android application that will take reviews from two different websites for single product, and analyze them with NLP for positive or negative rating. In this, user will give two different URLs of two different sites for same product to the system as input. For every URL reviews and comments will be fetched separately and analyzed with NLP for positive negative rating. Then their rating will be combined together with average to give final rating for the product. As we are handling the big data here, we are using Hadoops map reduce. So it will be easier to decide which product reviews are fraud or not.
AWS August Webinar Series - Analyze Mobile App Data and Build Predictive Appl...Amazon Web Services
Amazon Mobile Analytics helps you track key trends such as active users, revenue, retention, and behavioral insights.
In this webinar, you will learn how to make the most of your mobile analytics, and make better business decisions. The webinar will cover several use cases and business intelligence tracks such as Dashboards, Custom Analytics using queries, Data Visualization, and Machine Learning.
Learning Objectives:
• Make the most of app usage data by going beyond standard metrics such as Active Users, Sessions, Retention, etc. and performing custom analysis
• Leverage app data to build predictive algorithms to influence user engagement and monetization in the app
• Learn how to leverage existing tools to visualize and analyze data, and driven business decisions
Who Should Attend:
• Mobile App and Game Developers, Growth Hackers, Product Managers, Marketing Analysts, Data Analysts, Business Intelligence Engineers
A/B Testing best practices from strategic vision to operational considerations to communication and finally expectations management. We need to adhere to fundamental project management, technology, statistical, experimental design, UX Design, Customer Relationship, business and data principles to ensure that the insights and hence the decision is as trustworthy as possible.
A Practical Guide to Measuring User ExperienceRichard Dalton
Measuring the success (or failure) of an experience can be a daunting and confusing endeavor. In this presentation Richard shares 11 guidelines to help you quantitatively measure your user experience.
Richard provides techniques and tips for each phase and illustrates their use with real examples from his team’s work at Vanguard. To conclude, he describes some of the cultural and change management challenges involved when an organization uses data to inform design decisions.
Fairness in Search & RecSys 네이버 검색 콜로키움 김진영Jin Young Kim
검색 및 추천 시스템의 사회적 역할이 커지면서, 그 결과의 공정성 역시 최근 관심사로 대두되었다. 본 발표에서는 검색 및 추천시스템의 공정성 이슈 및 그 해법을 다룬다. 공정한 검색 및 추천 결과를 정의하는 다양한 방법, 공정성의 결여가 미치는 자원 배분 및 스테레오타이핑 문제, 그리고 검색 및 추천시스템 개발의 각 단계별로 어떤 해결책이 있는지를 최신 연구 중심으로 살펴본다. 마지막으로 실제 공정한 시스템 개발을 위한 실무적인 고려사항을 다룬다.
Measuring the Quality of Online Service - Jinyoung kimJin Young Kim
This document discusses methods for measuring the quality of online services. It describes how major companies like Google, Facebook, and Netflix collect data through user behavior, panel surveys, and direct user feedback at different stages of their services. Panel surveys can provide insights but have limitations, while user behavior data is abundant but noisy. The document also provides examples of how to design panel surveys and side-by-side evaluations to assess search engine result pages. It concludes that the best approach is to combine various data collection methods depending on the service characteristics and lifecycle.
Scribd, inc. slide share - google play - worldwide - 2021-05-03 18-07_11Noômen Ben Hassin
The document discusses how to use App Review Management to support mobile app growth. It notes that responding to reviews can increase customer satisfaction and retention, improve the product, and save time and money. It provides an overview of a mobile app's ratings and reviews and recommends automating responses to repetitive reviews, addressing unanswered reviews, and integrating helpdesk tools to streamline support and increase productivity. The goal is to increase organic growth, in-app transactions, and average lifetime customer value through higher ratings and improved customer experience.
Most of us use e-shopping (Any product) these days and refer its rating or reviews before we download or buy that product. Amazon/Play store provide a great number of products but unfortunately few of those product reviews are fraud. Hence such products must be marked, so that they will be recognizable for rest of the users. Here we are comparing reviews from two sites so that we can get more clear idea. We can get higher probability of getting real reviews if we take data from multiple sites. We are proposing a system to develop an android application that will take reviews from two different websites for single product, and analyze them with NLP for positive or negative rating. In this, user will give two different URLs of two different sites for same product to the system as input. For every URL reviews and comments will be fetched separately and analyzed with NLP for positive negative rating. Then their rating will be combined together with average to give final rating for the product. As we are handling the big data here, we are using Hadoops map reduce. So it will be easier to decide which product reviews are fraud or not.
AWS August Webinar Series - Analyze Mobile App Data and Build Predictive Appl...Amazon Web Services
Amazon Mobile Analytics helps you track key trends such as active users, revenue, retention, and behavioral insights.
In this webinar, you will learn how to make the most of your mobile analytics, and make better business decisions. The webinar will cover several use cases and business intelligence tracks such as Dashboards, Custom Analytics using queries, Data Visualization, and Machine Learning.
Learning Objectives:
• Make the most of app usage data by going beyond standard metrics such as Active Users, Sessions, Retention, etc. and performing custom analysis
• Leverage app data to build predictive algorithms to influence user engagement and monetization in the app
• Learn how to leverage existing tools to visualize and analyze data, and driven business decisions
Who Should Attend:
• Mobile App and Game Developers, Growth Hackers, Product Managers, Marketing Analysts, Data Analysts, Business Intelligence Engineers
A/B Testing best practices from strategic vision to operational considerations to communication and finally expectations management. We need to adhere to fundamental project management, technology, statistical, experimental design, UX Design, Customer Relationship, business and data principles to ensure that the insights and hence the decision is as trustworthy as possible.
A Practical Guide to Measuring User ExperienceRichard Dalton
Measuring the success (or failure) of an experience can be a daunting and confusing endeavor. In this presentation Richard shares 11 guidelines to help you quantitatively measure your user experience.
Richard provides techniques and tips for each phase and illustrates their use with real examples from his team’s work at Vanguard. To conclude, he describes some of the cultural and change management challenges involved when an organization uses data to inform design decisions.
The document discusses testing of the healthcare.gov website which cost $840 million to build but had only 6 users register on the first day. It went over budget to $2 billion and took 2 months to recover technically. The document provides an overview of testing types like functionality, usability, compatibility, performance, security, localization, and 508 compliance testing. It discusses concepts like critical path analysis, risk-based testing, mind mapping, and testing web applications using a case study of the Triangle App.
Opticon 2015 - Getting Started with the Optimizely Developer PlatformOptimizely
Speakers:
Jon Gaulding, Senior Software Engineer, Optimizely
Josiah Gaskin, Senior Software Engineer, Optimizely
Together we’ll walk through the Optimizely Developer Platform and use it to build a fully functional integration. We'll demonstrate how easy it is to bring any first or third party data source into Optimizely and use it for targeting and segmentation. Learn how to expose custom data in the native Optimizely interface for a truly simple user experience. Following the session, both speakers will be available in the developer’s sandbox area to help you write your own integration!
This document discusses how an experimentation program manager, Kristen, can empower teams within her organization to make data-driven decisions through experimentation at scale. It outlines three key challenges Kristen faces: teams cannot measure what matters to them, cannot leverage experiment results, and lack confidence in results. The document then presents solutions from Optimizely's data platform to address each challenge by allowing teams to: 1) measure custom metrics that are important to their work, 2) analyze results through their preferred tools and workflows, and 3) gain confidence in results by reducing biases, discrepancies and false discoveries. This will help Kristen grow and maintain adoption of her organization's experimentation program.
How to be Successful with Responsive Sites (Koombea & NGINX) - EnglishKoombea
Can't decide if your organization should build a mobile app or responsive website? Do you interact with consumer-facing products or large scale developments?
This guide gives you an idea of what Responsive is, why you should use it, and then DIGS deep into the technical aspect and how to optimize for performance.
By: David Bohorquez & Rick Nelson
Option 2015- Getting Started with Optimizely for MobileOptimizely
This document provides an overview of Optimizely for mobile apps. It discusses how mobile usage is growing rapidly and time spent on mobile devices now exceeds desktop. It then shares customer stories from French Girls and Secret Escapes who significantly improved key metrics like activation and signups through mobile A/B testing. The document demonstrates Optimizely's visual editor and code capabilities. It proposes a framework to test across acquisition, engagement, retention and monetization. Specific mobile testing ideas are outlined for each area like layouts, feature placement, checkout flows, permissions and reviews. Best practices are shared around integrating Optimizely, planning a testing roadmap, and verifying experiments.
Intro to Data Analytics with Oscar's Director of ProductProduct School
The Director of Product at Oscar, Vasudev Vadlamudi, went over key types of quantitative analysis that B2C product managers use on the job including: funnels, cohorts, and a/b testing. For each one he looked into when and why they are used, and used examples.
A "how-to" guide on creating successful websites or Apps. The three elements are (1) Design for User Tasks (2) Design for Flexibility (3) Design for Measurement. The presentation introduces numerous practical methodologies, e.g., The Application Mountain, The Water Mill Development Model, Onion Governance, and the User Task Matrix. Practical examples are taken from work at Scandinavian Airlines, Tryg Insurance, and Maersk.
Practical Tips for Ops: End User MonitoringDynatrace
Practical Tips for Ops: End User Monitoring
Watch replay here: https://info.dynatrace.com/apm_wc_devops_journey_series_end_user_monitoring_na_registration.html
Companies that have adopted DevOps Best Practices have 2555x faster lead times* in delivering new features to their end users. However, speed of delivery is not the only success metric! Success must also be measured on how end-users react to the speed of innovation.
Getting insights into how your end-users react to the changes you deploy allows you to share valuable feedback to the Dev and Biz teams. The teams can then see clearly how their changes impacted end-users and where fine tuning can improve infrastructure performance.
In this webcast Andreas Grabner, Chief DevOps Activist, and Brian Chandler, Systems Engineer, share practical tips that IT groups can start to implement quickly. You'll learn:
• Best approach for monitoring end-user mobile versus desktop versus tablet versus service end-points
• How to evaluate network bandwidth requirements by app, service and feature; to better understand and optimize resource consumption
• How to optimize your delivery chain in depth by understanding who is using your app, where, and on what device
• Clear view on which features are being used the most, the least, and what kind of behavior can be observed that is useful in tuning performance
If you are stuck in analysis paralysis, get insights that you can apply today!
*In addition, companies using DevOps are two times more likely to exceed profitability, market share and productivity goals (from the State of DevOps report by Puppet Labs 2016)
Corso Interazione Uomo Macchina e Sviluppo Applicazioni Mobile - GoBusAlessandro Longo
This document summarizes the results of usability testing conducted on the GoBus mobile application. Usability tests were performed with 10 novice users and 10 expert users completing tasks in GoBus and its competitor Moovit. The results found that users took fewer steps, less time, and made fewer errors to complete tasks in GoBus compared to Moovit, indicating that GoBus had better usability. Statistical analysis confirmed the test results were significant. Future work is planned to improve additional features of GoBus.
Why Apps Succeed: 4 Keys to Winning the Digital Quality GameAustin Marie Gay
Every company with a digital presence aims at delivering a great digital experience. But why do some web and mobile apps succeed better than others? As part of our ongoing search to find out, we surveyed over 1,000 technical experts and business leaders from various industries.
Join us for a live webinar as we discuss the findings of this report with experts from Perfecto, Cigna and Shop.com! Topics include:
-The four main obstacles preventing digital success and how to overcome them
-How web & mobile teams are organized to meet the demand for faster releases
-The digital testing strategies that increase velocity and allow teams to keep up with consumer demand
-Why automation and real-user condition testing is critical for achieving success
1) The document discusses UI/UX insights and case studies from work with various clients. It covers topics like requirements gathering, user research, prototyping, design, and delivery.
2) A sample persona for a customer success manager is described, including her goals, tasks, and tools used.
3) The outcome of one project is discussed, where mobile optimization and an analytical monitoring application were developed based on research and collaborative design processes. The client was happy with the results.
UserZoom Webinar: How to Conduct Web Customer Experience BenchmarkingUserZoom
You can't manage what you can't measure, so... How do you actually measure user experience?
In this webinar we covered what, why, and how to conduct website user experience & usability benchmarking. We discussed how to effectively measure the quality of a website's user experience across various competitors, within one industry, across time, using an online quantitative research methodology commonly referred to as "unmoderated remote usability testing."
Shared at "Data-Driven Design for User Experience" with Le Wagon Tokyo, 25 Aug
https://www.meetup.com/ja-JP/Le-Wagon-Tokyo-Coding-Station/events/280067831/
In UX design, data means the voice of users (customers) and actionable insights that are beyond just numbers. Hearing these voices through user research and usage analytics is a critical process of building a human-centric design. Based on data-driven design, UX designers, product managers, and even senior management can listen to the inner voice of users and extrapolate those to discover a user journey for clear call-to-action and unwavering customer loyalty.
At this webinar, our guest speaker Emi Kwon, UX Design Director at Metlife, will walk you through the basics of data-driven design as well as share some tips and tricks for making data-driven design your value proposition as a product manager/ UX specialist.
Agenda:
✔️ Data ecosystem — Data lake, data warehouse…what does it mean for UX?
✔️ Small data and big data — the opportunities and pitfalls
✔️ Research method basics — qualitative, quantitative or triangulated
✔️ Usage analytics and A/B testing
✔️ What about COVID-19 and remote usability testing?
Why Apps Succeed: 4 Keys to Winning the Digital Quality GamePerfecto by Perforce
Every company with a digital presence aims at delivering a great digital experience. But why do some web and mobile apps succeed better than others? As part of our ongoing search to find out, we surveyed over 1,000 technical experts and business leaders from various industries.
Join us for a live webinar as we discuss the findings of this report with experts from Perfecto, Cigna and Shop.com! Topics include:
-The four main obstacles preventing digital success and how to overcome them
-How web & mobile teams are organized to meet the demand for faster releases
-The digital testing strategies that increase velocity and allow teams to keep up with consumer demand
-Why automation and real-user condition testing is critical for achieving success
Secrets of going codeless - How to build enterprise apps without codingNewton Day Uploads
The document discusses methods for building enterprise applications without coding, known as codeless development or CAAD (Computer Aided Applications Development). It describes how CAAD uses pre-built components and templates to create applications through a point-and-click interface, removing the need for extensive programming. The methodology involves four phases - plan, develop, release, and review. Key aspects of the CAAD process include defining the application purpose and requirements, prototyping the design, user testing iterations, and ongoing maintenance after release. Site constructs provide predefined user interface elements to help bridge communication gaps between technical and non-technical users.
IRJET- Popularity based Recommender Sytsem for Google MapsIRJET Journal
This document describes a popularity-based recommender system for Google Maps that provides location recommendations to users based on their preferences and past visits. It uses machine learning algorithms like collaborative filtering to analyze user data and recommend popular places around a user's current location. The system aims to reduce information overload by learning from a user's profile and past actions to suggest new and interesting nearby locations like restaurants, parks, museums etc. based on what similar users enjoyed. It describes the framework, which collects a user's ratings for different places through a questionnaire, calculates ratings, predicts suitable places and provides recommendations on Google Maps. The goal is to accurately recommend locations tailored to each user's interests.
SenchaCon 2016: Using Ext JS 6 for Cross-Platform Development on Mobile - And...Sencha
Andrew will share the experience he and his team have gotten from using Ext JS 6 for cross-platform app development on mobile and desktop devices. Having released Ext JS 6 apps for industries including utilities, oil & gas, construction, and transport, Andrew has learned the common pitfalls and gotchas you need to know. Using examples from recent projects, he provides tips for those who aren't yet familiar or experienced with mobile development. By the end of the session, you will know how to think like a mobile developer, develop apps with mobile user experience in mind, make the move from Ext JS 5 or Sencha Touch to Ext JS 6, deploy native apps with Cordova, and manage data on devices with limited resources.
This document discusses feature engineering and machine learning approaches for predicting customer behavior. It begins with an overview of feature engineering, including how it is used for image recognition, text mining, and generating new variables from existing data. The document then discusses challenges with artificial intelligence and machine learning models, particularly around explainability. It concludes that for smaller datasets, feature engineering can improve predictive performance more than complex machine learning models, while large datasets are better suited to machine learning approaches. Testing on a small travel acquisition dataset confirmed that traditional models with feature engineering outperformed neural networks.
Data Analytics in Digital TransformationMukund Babbar
Pivotal helps large companies undergo digital transformation by implementing agile development methodologies and using data analytics. Pivotal works with one-third of Fortune 100 companies to build cloud native applications on their platform using microservices and data science. Case studies show how Pivotal has helped companies gain insights from data to improve customer experience through predictive models for network issues, call routing, and predictive maintenance.
This document contains the resume of Akshay Salvi. He has over 6 years of experience as a Senior Software QA Tester, specializing in manual testing of web applications, mainframes, and databases. He is ISTQB certified and has extensive experience with tools like HP ALM, Jira, and databases like DB2 and Oracle. His experience includes projects for clients like BMW NA and CareCentrix in domains like automotive and healthcare. He has experience in agile testing methodologies like Scrum and has taken on project lead roles as well.
The document discusses testing of the healthcare.gov website which cost $840 million to build but had only 6 users register on the first day. It went over budget to $2 billion and took 2 months to recover technically. The document provides an overview of testing types like functionality, usability, compatibility, performance, security, localization, and 508 compliance testing. It discusses concepts like critical path analysis, risk-based testing, mind mapping, and testing web applications using a case study of the Triangle App.
Opticon 2015 - Getting Started with the Optimizely Developer PlatformOptimizely
Speakers:
Jon Gaulding, Senior Software Engineer, Optimizely
Josiah Gaskin, Senior Software Engineer, Optimizely
Together we’ll walk through the Optimizely Developer Platform and use it to build a fully functional integration. We'll demonstrate how easy it is to bring any first or third party data source into Optimizely and use it for targeting and segmentation. Learn how to expose custom data in the native Optimizely interface for a truly simple user experience. Following the session, both speakers will be available in the developer’s sandbox area to help you write your own integration!
This document discusses how an experimentation program manager, Kristen, can empower teams within her organization to make data-driven decisions through experimentation at scale. It outlines three key challenges Kristen faces: teams cannot measure what matters to them, cannot leverage experiment results, and lack confidence in results. The document then presents solutions from Optimizely's data platform to address each challenge by allowing teams to: 1) measure custom metrics that are important to their work, 2) analyze results through their preferred tools and workflows, and 3) gain confidence in results by reducing biases, discrepancies and false discoveries. This will help Kristen grow and maintain adoption of her organization's experimentation program.
How to be Successful with Responsive Sites (Koombea & NGINX) - EnglishKoombea
Can't decide if your organization should build a mobile app or responsive website? Do you interact with consumer-facing products or large scale developments?
This guide gives you an idea of what Responsive is, why you should use it, and then DIGS deep into the technical aspect and how to optimize for performance.
By: David Bohorquez & Rick Nelson
Option 2015- Getting Started with Optimizely for MobileOptimizely
This document provides an overview of Optimizely for mobile apps. It discusses how mobile usage is growing rapidly and time spent on mobile devices now exceeds desktop. It then shares customer stories from French Girls and Secret Escapes who significantly improved key metrics like activation and signups through mobile A/B testing. The document demonstrates Optimizely's visual editor and code capabilities. It proposes a framework to test across acquisition, engagement, retention and monetization. Specific mobile testing ideas are outlined for each area like layouts, feature placement, checkout flows, permissions and reviews. Best practices are shared around integrating Optimizely, planning a testing roadmap, and verifying experiments.
Intro to Data Analytics with Oscar's Director of ProductProduct School
The Director of Product at Oscar, Vasudev Vadlamudi, went over key types of quantitative analysis that B2C product managers use on the job including: funnels, cohorts, and a/b testing. For each one he looked into when and why they are used, and used examples.
A "how-to" guide on creating successful websites or Apps. The three elements are (1) Design for User Tasks (2) Design for Flexibility (3) Design for Measurement. The presentation introduces numerous practical methodologies, e.g., The Application Mountain, The Water Mill Development Model, Onion Governance, and the User Task Matrix. Practical examples are taken from work at Scandinavian Airlines, Tryg Insurance, and Maersk.
Practical Tips for Ops: End User MonitoringDynatrace
Practical Tips for Ops: End User Monitoring
Watch replay here: https://info.dynatrace.com/apm_wc_devops_journey_series_end_user_monitoring_na_registration.html
Companies that have adopted DevOps Best Practices have 2555x faster lead times* in delivering new features to their end users. However, speed of delivery is not the only success metric! Success must also be measured on how end-users react to the speed of innovation.
Getting insights into how your end-users react to the changes you deploy allows you to share valuable feedback to the Dev and Biz teams. The teams can then see clearly how their changes impacted end-users and where fine tuning can improve infrastructure performance.
In this webcast Andreas Grabner, Chief DevOps Activist, and Brian Chandler, Systems Engineer, share practical tips that IT groups can start to implement quickly. You'll learn:
• Best approach for monitoring end-user mobile versus desktop versus tablet versus service end-points
• How to evaluate network bandwidth requirements by app, service and feature; to better understand and optimize resource consumption
• How to optimize your delivery chain in depth by understanding who is using your app, where, and on what device
• Clear view on which features are being used the most, the least, and what kind of behavior can be observed that is useful in tuning performance
If you are stuck in analysis paralysis, get insights that you can apply today!
*In addition, companies using DevOps are two times more likely to exceed profitability, market share and productivity goals (from the State of DevOps report by Puppet Labs 2016)
Corso Interazione Uomo Macchina e Sviluppo Applicazioni Mobile - GoBusAlessandro Longo
This document summarizes the results of usability testing conducted on the GoBus mobile application. Usability tests were performed with 10 novice users and 10 expert users completing tasks in GoBus and its competitor Moovit. The results found that users took fewer steps, less time, and made fewer errors to complete tasks in GoBus compared to Moovit, indicating that GoBus had better usability. Statistical analysis confirmed the test results were significant. Future work is planned to improve additional features of GoBus.
Why Apps Succeed: 4 Keys to Winning the Digital Quality GameAustin Marie Gay
Every company with a digital presence aims at delivering a great digital experience. But why do some web and mobile apps succeed better than others? As part of our ongoing search to find out, we surveyed over 1,000 technical experts and business leaders from various industries.
Join us for a live webinar as we discuss the findings of this report with experts from Perfecto, Cigna and Shop.com! Topics include:
-The four main obstacles preventing digital success and how to overcome them
-How web & mobile teams are organized to meet the demand for faster releases
-The digital testing strategies that increase velocity and allow teams to keep up with consumer demand
-Why automation and real-user condition testing is critical for achieving success
1) The document discusses UI/UX insights and case studies from work with various clients. It covers topics like requirements gathering, user research, prototyping, design, and delivery.
2) A sample persona for a customer success manager is described, including her goals, tasks, and tools used.
3) The outcome of one project is discussed, where mobile optimization and an analytical monitoring application were developed based on research and collaborative design processes. The client was happy with the results.
UserZoom Webinar: How to Conduct Web Customer Experience BenchmarkingUserZoom
You can't manage what you can't measure, so... How do you actually measure user experience?
In this webinar we covered what, why, and how to conduct website user experience & usability benchmarking. We discussed how to effectively measure the quality of a website's user experience across various competitors, within one industry, across time, using an online quantitative research methodology commonly referred to as "unmoderated remote usability testing."
Shared at "Data-Driven Design for User Experience" with Le Wagon Tokyo, 25 Aug
https://www.meetup.com/ja-JP/Le-Wagon-Tokyo-Coding-Station/events/280067831/
In UX design, data means the voice of users (customers) and actionable insights that are beyond just numbers. Hearing these voices through user research and usage analytics is a critical process of building a human-centric design. Based on data-driven design, UX designers, product managers, and even senior management can listen to the inner voice of users and extrapolate those to discover a user journey for clear call-to-action and unwavering customer loyalty.
At this webinar, our guest speaker Emi Kwon, UX Design Director at Metlife, will walk you through the basics of data-driven design as well as share some tips and tricks for making data-driven design your value proposition as a product manager/ UX specialist.
Agenda:
✔️ Data ecosystem — Data lake, data warehouse…what does it mean for UX?
✔️ Small data and big data — the opportunities and pitfalls
✔️ Research method basics — qualitative, quantitative or triangulated
✔️ Usage analytics and A/B testing
✔️ What about COVID-19 and remote usability testing?
Why Apps Succeed: 4 Keys to Winning the Digital Quality GamePerfecto by Perforce
Every company with a digital presence aims at delivering a great digital experience. But why do some web and mobile apps succeed better than others? As part of our ongoing search to find out, we surveyed over 1,000 technical experts and business leaders from various industries.
Join us for a live webinar as we discuss the findings of this report with experts from Perfecto, Cigna and Shop.com! Topics include:
-The four main obstacles preventing digital success and how to overcome them
-How web & mobile teams are organized to meet the demand for faster releases
-The digital testing strategies that increase velocity and allow teams to keep up with consumer demand
-Why automation and real-user condition testing is critical for achieving success
Secrets of going codeless - How to build enterprise apps without codingNewton Day Uploads
The document discusses methods for building enterprise applications without coding, known as codeless development or CAAD (Computer Aided Applications Development). It describes how CAAD uses pre-built components and templates to create applications through a point-and-click interface, removing the need for extensive programming. The methodology involves four phases - plan, develop, release, and review. Key aspects of the CAAD process include defining the application purpose and requirements, prototyping the design, user testing iterations, and ongoing maintenance after release. Site constructs provide predefined user interface elements to help bridge communication gaps between technical and non-technical users.
IRJET- Popularity based Recommender Sytsem for Google MapsIRJET Journal
This document describes a popularity-based recommender system for Google Maps that provides location recommendations to users based on their preferences and past visits. It uses machine learning algorithms like collaborative filtering to analyze user data and recommend popular places around a user's current location. The system aims to reduce information overload by learning from a user's profile and past actions to suggest new and interesting nearby locations like restaurants, parks, museums etc. based on what similar users enjoyed. It describes the framework, which collects a user's ratings for different places through a questionnaire, calculates ratings, predicts suitable places and provides recommendations on Google Maps. The goal is to accurately recommend locations tailored to each user's interests.
SenchaCon 2016: Using Ext JS 6 for Cross-Platform Development on Mobile - And...Sencha
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Similar to Data Science for Online Services: Problems & Frontiers (Changbal Conference 2021) (20)
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http://www.hellodatascience.com/
이벤트에 관련된 좀더 자세한 사항은 온오프믹스 링크 참조하세요: http://onoffmix.com/event/59334
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https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
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https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
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Data Science for Online Services: Problems & Frontiers (Changbal Conference 2021)
1. Data Science for Online Services:
Problems & Frontiers
Jinyoung Kim
Data & Analytics
Naver Search US
2021 Changbal Conference
2. About Me
Head of Data Science @ Naver Search
Executive Director of Naver Search US
Ex-MSFT / Ex-Snap (Search & RecSys)
Co-founder / 1st President of Changbal
* 한국/미국에서 Data Scientist & Engineer
채용중입니다! (jin.y.kim@navercorp.com)
https://medium.com/naver-dna-tech-blog
9. Challenge#2: Ecosystem / Social Impact
• Online services need to look
beyond user satisfaction
• Search / RecSys results can instill
and reinforce social bias
• Algorithmic ranking means
life/death for content providers
• Manipulating algorithmic results
became an industry
10. Solutions to Bias / Fairness in Ranking
Source: http://naversearchconf.naver.com/
11. Challenge#3: Internal Alignment
• Major online service
companies have dozens
of teams with different
focus
• Blind optimization in one
area can lead to negative
user experience
14. Problems & Solutions by Service Lifecycle
• Each stage present different analysis and decision problems,
with corresponding data science solutions
PLAN DEVELOP LAUNCH MONITOR
Defect
Monitoring
Competitive
Analysis
Offline
Experiment
A/B
Experiment
KPI
Monitoring
Opportunity
Analysis
Parameter
Optimization
Crowdsourced
Evaluation
15. DS Problems during Planning Stage
Questions
• How can we acquire &
onboard new users?
• What are the pain points
with existing customers?
• Where are we lagging
behind competing
services?
Solutions
• User funnel / journey
analysis
• Side-by-side comparison
• In-depth user study
16. Where do users come & go? Funnel Analysis
• New Users for E-Commerce • Existing Users for Contents
Discovery (Search / App Store)
Connect / Install
Create Account
Checkout
Revisit
App Open
Page View (browsing)
Page View
Consume
Revisit
Where do most users drop off the funnel?
Which part of the funnel has the biggest leverage?
How is the funnel shifting over time?
17. Beyond Funnel: User Journey Analysis
• Sanky chart can visualize diverging / converging user journey
Source: Medium
18. Example: Analyzing Snapchat User Journey
• Use app events to build & predict user journey graph
Source: Characterizing and Forecasting User Engagement
with In-app Action Graph: A Case Study of Snapchat
20. Applications for
Naver Mobile App
• What does overall user
journey look like?
• Can we understand
which factors improve /
hinder user satisfaction?
• Can we optimize user
experience, thereby
improving KPI (DAU)?
21. DS Problems during Dev. / Launch Stage
Questions
• Is the new
design/ranking better
than the old one?
• How can we choose the
best design/ranking
parameters?
Solutions
• Online (A/B)
experiment
• Multi-armed Bandit
22. Before/After Comparision vs. AB Test
• Hard to measure true impact of given feature
• High user impact in case of full launch & roll-back
Control Treatment
External Factors
(day-of-week / seasonality / ...)
Full Launch Roll-back
Control
100%
Traffic
23. Before/After Comparison vs. AB Test
• AB testing allows measurement w/o impact of external factors
• Multiple treatments with versioning & roll-out support
Control
Treatment1 (V1)
AB Test V1
External Factors (*)
(* no impact under randomized controlled experiment)
Roll-out &
Monitoring
Control
Treatment1 (V2)
AB Test V2
Treatment2
(gradual roll-out)
Problem
Solving
10~20%
Traffic
Treatment2 (V1) Treatment2 (V2)
26. Can we use the user
bucketing idea for
dynamically optimize AI
model / UI parameters?
But I have hundreds of
parameters to choose
from. I’d also like to
minimize user impact
27. Contextual Bandits for Parameter Optimization
• Converge to the best action (max. reward) given a context
28. DS Problems during Monitoring Stage
Questions
•Has there been any
shift in user behavior /
metrics?
•Has the algo results
shown any new
defects?
Solutions
•Metric monitoring /
detection framework
•Results defect
detection framework
29. Metric Regression
Detection Framework
• Generating alerts for
metric regression is easy
• The hard part is
minimizing false positives
• Solutions to control false
discovery rate is available
Source: KDD’20 Paper from Microsoft
32. Closing Thoughts
• Modern / AI-powered online services are
more powerful, but along came greater
challenges and responsibilities.
• Data Science provides various solutions
throughout the lifecycle of online
services
• The choice of techniques depends on
specifics of the service (Contents /
Commerce / Social network / …)
• Learning and starting career in data
science is also more accessible than ever
(bootcamps, better tools and guides)
AI-
enable
d
Mobile
-first
Cloud-
backend
33. We’re hiring @ Naver Search US!
• Exciting Data Science &
Artificial Intelligence problems
across Naver & Line
• Best of both Korean and US
tech working culture
• Huge growth opportunities
(150+ people in 3-5 years)
• Locations in Seattle & Bay
Area (+remote options)
https://naver-career.gitbook.io/en/
Editor's Notes
Data-driven decision making and optimization is feasible throughout