Machine learning meets web development
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This document discusses how machine learning can be used by web developers, designers, and marketers in addition to product engineers. It provides examples of how machine learning can enable interactive data visualization for designers, A/B testing of website variations by developers to determine the best performing version, and mapping of products in multi-dimensional preference spaces based on user behavior logs to help marketers. The conclusion is that machine learning has applications beyond product engineering and can also benefit other roles in web development.
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Machine learning meets web development
- 1. Machine Learning meets Web Development Shuhei Iitsuka @tushuhei
- 2. ML is just for product engineers? Spam Mail Detection Image Recognition (OCR) Recommendation Engine Query Suggestion Auto Completion Image Tagging Word Indexing Fraud Detection Search Engine Voice Recognition Robot Locomotion Automatic Diagnosis Video Game Expert Automated Trading Face Recognition Pattern Recognition Route Search Trend Forecasting Handwriting Recognition Computer Vision Translation Name Identification Transaction Data Mining Human Modeling
- 3. Machine Learning is also for Web designers / developers / marketers.
- 4. ML for designers: interactive data visualization. Shuhei Iitsuka and Yutaka Matsuo: A Product Network Based on Browsing and Purchase Behavior in E- commerce. The institute of electronics information and communication engineers D. 2015. Wedding Venue A Wedding Venue B Images from: http://zexy.net/wedding/c_7770021671/ http://zexy.net/wedding/c_7770029193/ User inquiry inquiry Competition
- 5. ML for designers: interactive data visualization. http://colah.github.io/posts/2014-10-Visualizing-MNIST/ Example: Visualizing MNIST: An Exploration of Dimensionality Reduction Visualization of how a machine recognize handwritten digits to classify.
- 6. ML for developers: ask users for the best one. Image from: Autonomous Systems Labs - TU Darmstadt http://www.ausy.tu-darmstadt.de/Research/Research Website UsersVariation Clicks A/B testing
- 7. ML for developers: ask users for the best one. Example: Obama Campaign in 2010 $60M improvement http://blog.optimizely.com/2010/11/29/how-obama-raised-60-million-by- running-a-simple-experiment/ Example: A/B testing on Bing BEFORE $10M annual revenue improvement Iitsuka, Shuhei, and Yutaka Matsuo. "Website Optimization Problem and Its Solutions." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. AFTER
- 8. ML for marketers: product mapping on preference axis. item #1 item #2 ・ ・ ・ item #10000 user #1, ・・・, user #m BUY SKIP SEE BUY SEE SKIP ・・・ ・・・ ・・・ ・・・ LOG DATA luxury modern Axis of preference modest old-fashioned User behavior Aggregation (PCA) item #1 item #5 item #3 item #2 item #4 item #7 item #6
- 9. Again, machine learning is not only for product engineers but also for designers / developers / marketers. Conclusion:
Editor's Notes
- Today, I’d like to talk about how we can utilize machine learning technology for website development.
- When we say machine learning, you may think it is about products. For example, when you use Gmail, spams are detected very well thanks to the spam detection technology. When you use Evernote, it can read the text from the picture thank to the great OCR technology. When you buy something on Amazon, it always recommends us some items you may like. Is there something to do with us, web designers, web developers, and web marketers?
- Actually, there is. Machine learning is not only for product engineers, but also for web designers, web developers, and web marketers. So, in this presentation, I will show some examples how we can use machine learning technologies for our daily development.
- The first example is for designers. Machine learning help you to create interactive contents with data visualization. This example shows a product network from one of the biggest Japanese wedding portal websites named Zexy. In this case, a product means a wedding venue, so this is the competitive network of wedding venues. Each wedding venue is represented by a dot. They are connected if they are seen by the same users or got inquiries from the same users. We can assume that they are in competition if they got interested by the same users. The color represents location of the wedding venue. So we can tell that they form the clusters of competition based on its location. This is not only beautiful, but also informative.
- This is not my work, but another great example of network visualization. We can implement 3D interactive network with WebGL technology.
- The second example is for developers. Sometimes we are not sure which design or feature is most attractive for users. In this case, we can ask users for the best one. In the field of robotics, there is a bunch of techniques to let robots act intelligently in its environment. Actually, we can also apply the technology to website optimization by assuming the website is a robot and the environment is users.
- Here are some examples of successful A/B testings. This is a famous one from Obama’s campaign in 2010. They tested 6 images and 4 button labels on its homepage to invite donation. By applying the best combination, they succeeded to get additional 60-million-dollar donation. Another example comes from Bing. They succeeded to improve the revenue by 10 million dollars by making this change. By the way, can you tell the difference? Actually, they changed the color of link texts a little bit darker. Small change can make a big difference.
- The final example is for marketers. product mapping on preference axis. Of course, log data is a treasure house to get benefit with data mining. Each user has their own preferences and it is reflected on their behavior, on log data. So, we can extract the axis of their preference by aggregating the log data with the technique called PCA. As a result, we can see the product positioning which is useful for marketing / branding.
- I showcased examples how we can use machine learning for web development. Again, machine learning is not just for product engineers but also for web designers, developers, and marketers. I hope this presentation inspired you to introduce machine learning technologies to your projects. Thank you.