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Amazon personalize personalized recommendation launched in Amazon cloud technology China

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It is convenient and fast to build personalized recommendation system without machine learning professional knowledge

Amazon personalize, a fully hosted machine learning service for building personalized recommendation system, is officially launched in Amazon cloud technology China (Beijing) region (operated by halo Xinnet). Using this service, developers do not need to have machine learning professional knowledge, users can use it to train, adjust and deploy their own customized machine learning model, build personalized recommendation system, and use it for a wide range of personalized recommendation scenarios, such as product recommendation, personalized marketing, personalized search and customized direct sales. visit You can start using Amazon personalize.

Gu fan, general manager of cloud service product management in Greater China region of Amazon cloud technology, said: “all along, the demand for recommendation system can be said to be everywhere. From e-commerce shopping, news reading, audio and video to online application recommendation, many companies hope to build personalized recommendation system to enhance user experience and increase business revenue. Building an accurate and effective personalized recommendation system needs to solve many technical challenges, such as machine learning algorithm, model and so on. We are very happy to launch Amazon personalize in China through close cooperation with halo Xinnet, which reduces the threshold of machine learning technology and enables customers to focus on their own business innovation. Without in-depth understanding of machine learning, we can build our own personalized recommendation system and enjoy the convenience brought by artificial intelligence. “

Back in 1998, Amazon e-commerce launched a collaborative filtering algorithm based on items, which is the first time in the industry to apply the recommendation system to millions of items and millions of users. This is the innovation that later became famous in the industry – Amazon e-commerce’s personalized recommendation of “thousands of people and thousands of faces”. Amazon personalize refines Amazon’s 20 years of innovative practice and experience in machine learning, empowers all industries, enterprises of all sizes, developers and data scientists to shorten the time of building customized models from a few months to a few days.

Using Amazon personalize, developers only need to provide user behavior information such as page browsing, registration or purchase, and tell Amazon personalize whether the list of items to recommend is music, video, product or news article, then they can receive the recommended results through the application programming interface (API). Amazon personalize will process and check the data, identify the meaningful content, select the algorithm from the advanced algorithm library built by Amazon e-commerce retail business for many years, and train and optimize the personalized model according to customer data. Throughout the process, all data is encrypted to ensure privacy and security, and is only used to create recommendations for users.

Amazon personalize pre sets the necessary infrastructure and manages the entire machine learning pipeline, including processing data, determining functions, using the best algorithms, and training, optimizing, and hosting models. Customers receive results through API and pay according to usage without minimum consumption or prepayment commitment.

Youdao happy reading is a digital reading education product of Netease Youdao, which is dedicated to improving children’s reading literacy, and hopes to provide a reading experience from “thousands of people one side” to “thousands of people one side”. Youdaoledu technology has fewer developers and less experience in artificial intelligence. How to launch the recommendation system in a shorter time and save the learning cost is an important issue for the team to consider when selecting the model. Jiang Wei, Senior Server Development Engineer of youdaoledu, said, “with Amazon personalize, youdaoledu’s app R & D team has successfully built a precise recommendation scene for children’s books within one month, increasing monthly active users by 20%.”

As a subsidiary of Rakuten limited, Rakuten mart is a leading retailer in South Korea, selling all kinds of general merchandise, clothing, toys, electronic products and other commodities. Today, consumers have extremely rich channels to buy daily necessities, including hypermarkets, e-commerce platforms, convenience stores, supermarkets and so on. Lotte decided to use Amazon personalize to provide personalized coupon recommendations for old customers, so as to increase its store frequency, increase the purchase rate of new products, and ultimately strengthen customer loyalty. “Since the introduction of Amazon personalize, the usage of coupons has more than doubled compared with the previous rule-based statistical recommendation system,” said sungoh Park, an analyst with Lotte data. The purchase rate of new products has increased by 1.7 times, which is significantly higher than that of previous statistical methods. More importantly, the increase of new product purchase rate shows that Lotte Mart has successfully discovered the hidden purchase demand in the customer group. This new operation mode with personalized coupons as the carrier has significantly improved the monthly sales of Lotte Mart. “

Stockx is a start-up company from Detroit, which hopes to innovate the e-commerce system with a unique bid / bid market. The design of the platform is inspired by the New York Stock Exchange and regards commodities such as sports shoes and street fashion as high-value tradable commodities. With its transparent market trading experience, stockx helps consumers buy highly sought after authentic products at real market prices. Sam bean, founder member and head of Machine Learning Department of stockx, said: “in 2019, stockx is experiencing rapid growth, and our team of machine learning engineers are also trying to use Amazon personalize to add a” recommend for you “product recommendation line on the home page. Our team started the project development a few weeks before the holiday shopping season and brought it online in time for the shopping season. We can be proud to say that with the help of Amazon personalize, our microservice architecture showed nearly perfect usability throughout the holiday. In the end, the new feature became the most popular part of the home page. “Recommend it to you” has become a great victory for our team and the whole stockx company. ” Read more: how does Amazon and Netflix’s personalized recommendation work? Amazon cloud technology’s self-developed cloud native processor provides high cost-effective computing power for graffiti intelligence. Amazon graviton2 is equipped with self-developed processor. Amazon cloud technology in China is rich in ecological diversity. Amazon cloud technology announced that Amazon EC2 with self-developed processor Amazon graviton2 will provide Amazon EFS with low-cost storage level X2gd fully available big data driven e-commerce personalized recommendation (PPT) Amazon cloud technology releases China business strategy Tencent: only 24% of users think the personalized recommendation system is reliable. Looking at e-commerce from the perspective of U.S. retail industry (3) — technology interpretation of U.S. retail hotel tonight: mobile application is the future of tourism. How big is Google? Two billion lines of code, equal to 40 windows! Three trends of big data explosion promoting business and scientific change time: 50 best iPhone applications in 2012: instagram and other four American dramas selected to teach you to understand big data

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