Powered by the leading big data and artificial intelligence technologies of Alibaba, Alibaba Cloud Artificial Intelligence Recommendation (AIRec) provides personalized recommendation services for enterprises and developers. It is developed based on service accumulations in a variety of industries, such as e-commerce, content, news, live streaming, and social media. To obtain personalized recommendations, you need only to provide the required data and call related API operations.
The recommendation solutions of AIRec are classified by industry. The following industry templates are available: e-commerce, content, and news. New industry templates will be added based on customer requirements.
You can use this template to recommend items that have commodity attributes, such as logistics information and sales information. The recommendations can guide users through direct transactions and have specific requirements on the click-to-purchase ratio. Common apps include Taobao, Tmall, and Xianyu.
This template is suitable for content sharing platforms. You can use this template to recommend content that has sharing attributes, such as liking and forwarding. The recommended content can be short text, articles, images, or a combination of them. Common apps include Taobao Headlines.
You can use this template to recommend information that has news attributes, such as author, the news publish location, and the publish time. News is a method for spreading information and requires high timeliness. Common apps include UC Toutiao.
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This type of service is suitable for scenarios where the browsing intention of users is not clear. AIRec learns information about the interest shown by the long-term and short-term behavior of users. Then, AIRec runs training tasks to explore user interests and present diversified content recommendations. Common locations: homepage and product category page.
This type of service is suitable for scenarios where the interest of users has been basically determined. AIRec finds dynamically associated recommendations based on the focus of the interest, such as 1/N commodities or 1/N articles, as well as the results of calculation and analysis on the massive behavior data of users. Then, AIRec finds statically associated recommendations based on the correlation between the attributes and the features of the dynamically associated recommendations. Common location: details page.