3results about How to "Achieve recommendation" patented technology

This invention provides a method, apparatus, electronic device, and storage medium for generating product recommendation copy. The method includes acquiring fields of multiple products input by a user; using the fields of the multiple products as input to a text generation model; processing the fields of the multiple products based on the text generation model; and outputting product recommendation copy arranged in a specific order. The text generation model is trained using sample data. In this embodiment of the invention, the text generation model trained based on sample data can predict the fields of multiple products and output product recommendation copy arranged in a specific order, thereby enabling recommendations for multiple products and improving the logical consistency of product recommendation copy.

This application provides a method, apparatus, and medium for calculating the value of a business portfolio, relating to the field of information technology. The method includes: obtaining a first user characteristic factor, candidate product factors, and discount strategy factors; inputting the first user characteristic factor, candidate product factors, and discount strategy factors into a multi-factor combination optimization algorithm, which, based on a multi-level cache and / or a real-time computing engine, obtains an effective business portfolio suitable for the first user and its predicted value; and recommending, accepting, and monitoring the effective business portfolio based on the predicted value. The first user characteristic factor is obtained based on the characteristics of the first user, and the effective business portfolio is a combination of at least one candidate product and / or a combination of at least one candidate product and at least one discount strategy. This application helps improve the digital and intelligent operation level of enterprises.

A course recommendation method, device, equipment and medium are disclosed. The method comprises: acquiring actual interaction messages between a target user in real time; determining a current interest value of the target user according to the actual interaction messages and a user label of the target user; determining a corresponding target recommendation strategy according to a comparison result between the current interest value and a preset interest threshold value, and using the target recommendation strategy to recommend a course to the target user. The present application solves the problem that the course recommendation in the prior art is subjective and based on human experience, resulting in low recommendation pertinence, and achieves targeted course recommendation to the target user, improves the course recommendation efficiency, and enhances the user experience.