Powered by the big data and artificial intelligence technologies of Alibaba, Artificial Intelligence Recommendation (AIRec) provides personalized recommendation services for enterprises and developers. AIRec 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 to only call AIRec API operations by providing the required data.
Introduction
Industry templates
The recommendation solutions of AIRec are classified by industries. AIRec provides templates for the e-commerce, content, and news industries. Templates for more industries will be added based on customer requirements.
Template for the e-commerce industry
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.
Template for the content industry
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 and different marketing communities.
Template for the news industry
You can use this template to recommend information that has news attributes, such as the 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.
Service types
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This type of service is suitable for scenarios where the browsing intention of users is unclear. AIRec learns information about the interests shown by the long-term and short-term behaviors of users. Then, AIRec runs training tasks to explore user interests and present diversified content recommendations. Common locations: homepage and product category page.
Related recommendations
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: product details page.