Freestocks / flickr Pinterest, whose monthly active users reach 100 million, is increasingly relying on machine learning to help discover new Internet insights. Users around the world visit Pinterest to explore, save, and share photos and articles. To help users find what they like, users will naturally be retained: 30% of Pinterest's interactions and 25% of Pinterest's in-app purchases come from Pinterest's recommended content. In order to recommend suitable content, Pinterest used top technologies driven by data and conducted extensive experiments. So, how is Pinterest implemented using machine learning? Phamest’s lead discovery science engineer Mohammad Shahangian stated: “My main job is to find a solution to the problem of content discovery. We will experiment with very small changes to the algorithm. Every attempt has its improvement or not. Nice place." Exclusive advantage: based on interest Thestreet In fact, this is not unrelated to the features of Pinterset: One of its strengths is that Pinterset is a community built around the interests of users who categorize products, articles and pictures that they find on the Internet by interest. This means that Pinterest does not need to guess user interest by clicking on patterns or time spent on a page, like other social networking sites. Instead, it can directly use algorithms to measure the relationship between 75 billion entries in its database because These entries are likely to be grouped under the same interest. Mohammad Shahangian said: "A lot of companies are trying to derive user interest through input or signals. But at Pinterest, the user clearly gives a signal of what he is interested in." Users visiting Pinterest continue to contribute to this social picture of users, collected items, and favorites. This data, in turn, allows Pinterest to more accurately recommend content for user home page message flows, search results, and related content. Simply recommending to the user based on the content of the user's attention is not ideal, and recommending similar content is easy to repeat. According to Mohammad Shahangian, "If you collect an item for a kitchen sink, should we recommend more sinks for you, or do you recommend items that can make your kitchen look new?" Continuous testing in practice To make these decisions, Pinterest's engineers experimented with a variety of machine learning algorithms. They studied the effectiveness of these algorithms on related and unrelated items and how they affect the liveness of real users. Mohammad Shahangian said: "We do experiment directly on Pinterest, but many times we will do a lot of preparation before testing." Of course, without actual testing, there is no way to know if a user will like new recommendations. "I can't pay people to tell me whether a certain user will like the new recommendation," Mohammad Shahangian said. However, by studying whether the recommended content of the algorithm will be classified by a real user as a certain interest, a relatively reliable answer can be obtained. Previously, Pinterest changed the message flow of the user home page from a purely time-aligned user message to a message flow generated by the algorithm. This action increased the user's activity by one-tenth to one-tenth. Subsequent algorithm improvements will also bring additional improvements. Shahangian said: "Pinterest has made great progress throughout the improvement process. Personalization has greatly improved user activity." Improve image search Pinterest has also been improving image search to help users find similar images better. Pinterest engineers collaborated with researchers at the University of California Berkeley's Visual and Research Center to develop this technology. Now it has been able to automatically recognize objects in the picture through deep learning algorithms. In this way, the user can click on these objects to find related entries in Pinterest. Dmitry Kislyuk, Pinterest’s visual search engineer, said: “This is not a classification algorithm that distinguishes between cats and dogs. We want to find the similarities between pictures in real time.†He said that this visual search tool works well for finding home decorations and fashion items in Pinterest. In the future, Pinterest hopes to improve its automatic classification function to better meet other search needs, such as helping users find similar new recipes. When talking about classifying images more effectively with deep learning, Pinterest visual search engineer Andrew Zhai said: "I think our model will become more semantic and it will become better." While Pinterest's engineers are focusing on improving object recognition and search, they also plan to develop an application that allows smartphone users to shoot real-world objects and then get related entries on Pinterest. Dmitry Kislyuk said: “The field of deep learning and computer vision is exciting today. The world changes so fast that top technology changes every two months.†Via fastcompany Recommended reading: Pinterest Completes Series G Financing, Total $553 Million Fails to Expect Created a designer of the Apple Mac classic, will join Pinterest responsible for product design
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