一种内容推荐方法、内容特征提取方法及装置

CN122414366APending Publication Date: 2026-07-17HUAWEI TECH CO LTD +1

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUAWEI TECH CO LTD
Filing Date
2025-01-17
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing recommendation systems struggle to maintain performance when handling large-scale data and have limitations in handling complex feature interactions, thus affecting recommendation effectiveness.

Method used

By independently processing the semantic, location, and temporal information of the historical behavior sequences of users and content in the neural network model, and utilizing adaptive multi-channel self-attention layers and multi-stage divide-and-conquer feedforward layers, interaction relationships of different dimensions are modeled respectively, thereby improving the accuracy and efficiency of the recommendation system.

Benefits of technology

It enables finer-grained modeling of interaction relationships across different dimensions, improving the accuracy and efficiency of the recommendation system and providing more precise and personalized recommendation services.

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Abstract

一种内容推荐方法,包括:获取用户的历史行为序列中各行为项的语义嵌入向量、位置嵌入向量和时间嵌入向量;在神经网络模型中,对第一通道特征做注意力处理,以及,对第二通道特征做线性处理,以得到用户特征,其中,第一通道特征包括语义嵌入向量,第二通道特征包括位置嵌入向量和时间嵌入向量中的至少一项;基于用户特征,向用户推荐与用户的偏好相符的内容。这样,通过将历史行为序列中各行为项的语义信息、位置信息和时间信息中的至少一项剥离,就可以去掉位置和 / 或时间关系的限制,从而带来了更优的效率,且可实现更细粒度的建模不同维度的项目间的交互关系,带来更优的精度。
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