Content recommendation method and apparatus, computer device, and computer-readable storage medium
By performing attention feature mining and feature fusion processing on the content attribute information sequence, object feature information of the target object is generated, which solves the shortcomings of I2I and U2U recall in the existing technology and achieves more accurate content recommendation.
CN117725299BActive Publication Date: 2026-07-14XIAOHONGSHU TECH CO LTD
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Patent Information
- Application Number
- CN202311028969.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-15
- Publication Date
- 2026-07-14
- Estimated Expiration
- 2043-08-15
AI Technical Summary
Technical Problem
In existing content recommendation algorithms, I2I recall cannot take into account user behavior features, resulting in low accuracy of recommendation results, while U2U recall performs poorly in recommending new content.
Method used
Based on multiple historical interaction contents of the target object, the content attribute information sequence under various attributes is obtained. Through attention feature mining and feature fusion processing, the object feature information of the target object is generated, and then content is recommended to the target object.
Benefits of technology
It improves the accuracy of content recommendation, enabling more precise recommendations of relevant and useful content to the target audience.
✦ Generated by Eureka AI based on patent content.
Abstract
Embodiments of the present application disclose a content recommendation method and device, computer equipment and a computer readable storage medium. Based on a plurality of historical interaction contents of a target object, content attribute information sequences under a plurality of attributes are obtained, each content attribute information sequence including attribute information of the plurality of historical interaction contents under the same attribute. The content attribute information sequences are subjected to attention feature mining processing to obtain an attention feature information sequence corresponding to each attribute. The attention feature information sequences are subjected to first feature fusion processing to obtain a fused attention feature information sequence. A behavior feature information sequence of the target object is generated according to a target content attribute information sequence belonging to a content identifier attribute in the content attribute information sequences. The fused attention feature information sequence and the behavior feature information sequence are subjected to second feature fusion processing to obtain object feature information of the target object. The object feature information is used to recommend content to the target object, and the content can be accurately recommended.
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