Marketing information recommendation method, device, equipment, storage medium and program product
By acquiring users' historical mailing behavior data and using deep learning methods to predict the mailing situation of existing and similar users, the problem of reliance on courier experience is solved, and accurate recommendations for mailing services and precise push of marketing information are achieved.
CN122155767APending Publication Date: 2026-06-05BEIJING JINGDONG YUANSHENG TECH CO LTD
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Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING JINGDONG YUANSHENG TECH CO LTD
- Filing Date
- 2024-12-05
- Publication Date
- 2026-06-05
AI Technical Summary
Technical Problem
In existing technologies, couriers rely on their own experience to recommend mailing services, resulting in inaccurate recommendations and making it difficult to achieve precise mailing service recommendations.
Method used
By acquiring users' historical mailing behavior data, deep learning methods are used to predict the mailing patterns of existing and similar users. Based on the prediction results, marketing information is determined and pushed to target users.
Benefits of technology
It enables precise recommendations for mailing services, reduces the marketing difficulty for target users, and the recommendation results reflect users' mailing preferences.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN122155767A_ABST
Abstract
The application provides a marketing information recommendation method and device, electronic equipment, computer storage medium and computer program product. The method comprises: obtaining historical mailing behavior data of each user in a user set; the user set comprises inventory users and new users; predicting mailing conditions of the inventory users according to the historical mailing behavior data of the inventory users to obtain a first prediction result; determining similar users of the new users, predicting mailing conditions of the similar users according to the historical mailing behavior data of the new users to obtain a second prediction result; determining marketing information of the inventory users according to the first prediction result and determining marketing information of the similar users according to the second prediction result; and in response to a recommendation operation triggered for any target user in the inventory users and the similar users, pushing the marketing information of the target user.
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