At present, Pinterest has an average monthly active user volume of 100 million. How does this image-based company retain users and make money? Pinterest's main goal is to recommend relevant images or content to users, and the recommended content is accurate enough to improve user stickiness. Recently, Fast Company issued a statement saying that Pinterest is learning from machine learning, recommending more accurate content to users and expanding new online business. On the Pinterest platform, people can search and download images and articles across the network, and finding content that fits their preferences can naturally increase their user loyalty. Relevant content recommended by Pinterest increased participation by 30% and purchase by 25%. These precision recommendations are based on cutting-edge data-driven technology and the results of numerous experiments. Users can use the virtual nail board of Pinterest to collect online products, posts and pictures across the network. This means that the platform is completely based on user interest preferences. Pinterest doesn't have to guess its interests based on the user's click mode or specific page time, just like other social networks. This also means that its algorithm can infer the interrelated information in the 75 billion favorite content databases, because similar content is more easily fixed on the same nailboard, thus easily identifying user preferences. Pinterest can be said to be a social graph consisting of billions of interconnected users, the same project collected by different users, and virtual nailboards that collect similar projects. This composition also determines the number of users. increase. Mohammad Shahangian, senior discovery science engineer at Pinterest, said, "We have made minor corrections to our algorithms through hundreds of experiments to determine the direction in which the problem was discovered." It’s not ideal to simply decide which model to recommend content based on the user’s attention. Suppose a user is planning their own wedding. Her virtual nailboard adds a lot of pictures of the dress style, and her followers don’t necessarily need this. Class clothing, recommending a dress for them may cause meaningless repetition. All Pinterest data is available to Pinterest users. Shahangian said, “If your virtual nailboard has a kitchen sink link, do we need to push more than 10,000 kitchen sinks to you, or inspire how you can design your kitchen as a whole?†In this case To make the right judgment, the company's engineers tested a variety of machine learning algorithms to investigate how different formulas perform collections of similar or different test sets and how they ultimately affect real-world user engagement. However, in fact, Pinterest's technology development lacks case testing, and researchers cannot test whether he will accept a new set of recommendations by paying a specific user. Although the latter test process cannot be performed, the current algorithm can basically realize whether the person is willing to be a manual tester through the user's favorite content. Pintesrest has gained a lot of inspiration from its own development, and personalization has always been one of the biggest factors influencing user engagement. At the same time, the company has been working to improve visual search capabilities to help users get content similar to the target image. Earlier this year, Pinterest engineers worked with researchers from the University of California, Berkeley's Center for Visual and Learning to develop deep learning techniques that automatically detect image content. Pinterest's senior visual search engineer Dmitry Kislyuk said that when we try to distinguish whether the picture content is a cat or a dog, this is not a classification task. The key is to find the visual similarity between images under the premise of real-time. . The visual search tool is especially useful for collecting information on home décor and fashion items. In the future, companies want to improve their ability to map targeted content to their categories. For example, when users want to find new recipes for the same ingredients, don't just provide similar photos of the ingredients. Andrew Zhai, senior visual search engineer, points out, “I think our model can be more semantic and effectively apply deep learning to mapping more conceptual images. At the same time, Pinterest engineers are focusing on improving target detection and search. 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