融入用户意图的图交互新闻推荐模型

doi:10.3778/j.issn.1002-8331.2401-0039

摘要/Abstract

摘要： 用户的阅读喜好通常会受到人类主观心理活动——意图的驱使，现有的模型往往采用固定模式来解决特定问题，忽略了用户阅读文章时的真正意图，这使得用户部分建模不足。另一方面，该类模型缺少新闻与用户间的交互作用，致使新闻-用户表示不准确。针对上述问题，提出了一种融入用户意图的图交互新闻推荐模型。该模型利用图神经网络构建新闻语义扩充模块、用户意图模块以及用户兴趣模块，模块间具有交互作用，能够识别用户意图与意图转变，丰富候选新闻语义，获得意图增强的新闻-用户表示。在真实数据集MIND上的实验结果表明，此种融入用户意图的图交互式个性化新闻推荐模型可以有效预测出用户更感兴趣的文章，与前沿的新闻推荐模型相比效果有显著提升。

关键词: 图神经网络, 新闻语义扩充模块, 意图模块, 兴趣模块, 交互作用, 个性化新闻推荐

Abstract: Users’ reading preferences are usually driven by a high-level-intention, and existing models tend to adopt a fixed model to solve a specific problem, ignoring the real intention of users when they read an article, which makes the user part of the modeling insufficient. On the other hand, this type of model lacks the interaction between news and users, resulting in inaccurate news-user representation. A news recommendation model incorporating user intent in interactive graphics is proposed for the above issue. The model utilizes graph neural networks to construct a news semantic expansion module, a user intent module, and a user interest module, which interact with each other to recognize user intent and intent transformation, enrich candidate news semantics, and obtain intent-enhanced news-user representations. Experimental results on the real dataset MIND demonstrate that this user intent-incorporated interactive personalized news recommendation model can effectively predict articles that are more interesting to users, leading to a significant improvement in performance compared to state-of-the-art news recommendation models.

Key words: graph neural network, news semantic augmentation module, intent module, interest module, interaction, personalized news recommendation

刘桂红, 焦琛添. 融入用户意图的图交互新闻推荐模型[J]. 计算机工程与应用, 2025, 61(9): 159-167.

LIU Guihong, JIAO Chentian. Interactive News Recommendation Model Incorporating User Intent[J]. Computer Engineering and Applications, 2025, 61(9): 159-167.

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