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One-stop personalized recommendation solution

With only a few configurations, you can deploy and build your own highly accurate recommendation system with one click, enabling business growth.

Low code, full link, high precision personalized recommendation system

Based on advanced big data and artificial intelligence technology, it can help enterprises overcome code problems, build personalized recommendation systems quickly and painlessly without ML expertise, improve click-through rate and retention rates, and boost business growth

Easily deploy and use

Even without the background of AI technology, the personalized recommendation system can be rapidly deployed in a low-code way to obtain accurate, personalized recommendation results.

Based on advanced AI technology

Based on the advanced AI technology, it fully uses user interaction behavior, text description, image, and visual information to deeply understand products and users and help stores more accurately capture the potential needs of users.

One-stop personalized solution

It supports a variety of application scenarios, such as guessing what you like, recommending similar products, and watching again. A full link enables the store to explore the value of the store entirely.

Low code, efficient, fast deployment of industrial recommendation system

Bert, Spring, Item CF, and other machine learning algorithms are used to optimize based on different industries and massive data, with high accuracy and pertinency. Non-ai professionals can also rapidly deploy low code and improve key operational indicators such as click and conversion rates.

Break through the complex pain points in a personalized recommendation system's deployment process and build a high-precision recommendation system quickly and conveniently.

Convenient operation

Customers only need to provide user, commodity, and interactive behavior data and define the data fields to get a complete set of recommendation systems.

Using flexible

The online service and offline training frameworks use the k8s virtualization technology to facilitate system deployment, expansion, and disaster recovery.

Results the accurate

Based on the MetaSpore machine learning platform, the system supports classical and large-scale deep learning models. At the same time, the system has multiple recommendation strategy templates, which customers can select and expand according to the AB Test results.

E-commerce recommendation

You can import all data quickly and efficiently with a few operations. The industry-grade recommendation system brings users different homepage experiences -- guess what you like, look and look, post-purchase link, and other scenarios, enabling users to discover products faster and more easily, creating first-class browsing and consumption experience, promoting purchase decisions, and achieving business growth.

A variety of machine learning algorithms, all-round help high precision personalized recommendation

Overseas E-commerce Personalized Recommendation

Just click, and you can install it in the store. The whole process is simple and fast. Dimensional analysis based on AI technology users, commodity information, the shop front page, album, business details page, shopping cart pages recommendation of personalized recommendation, similar products such as display, enabling users to browse more interested in goods quickly, the value of flow, improve click-through rates, conversion rates, DAU key operational indicators, such as increase user stickiness.

Scenario Practice

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