面向用户动态偏好的科技论文推荐：一种基于注意嵌入的知识图谱方法

摘要： 科技论文推荐系统是解决论文数据库中信息过载的有效途径。本研究提出了一种基于注意嵌入的知识图谱方法用于科技论文推荐任务，以提升论文推荐的效果。首先构建一个协同知识图谱以整合研究人员行为与论文属性信息，并通过TransR方法优化节点向量表达；其次引入注意序列模块，通过注意传播机制学习节点特征，并利用序列注意机制从阅读序列中捕捉研究人员的时序偏好；最后，模型通过计算研究人员与候选论文之间的匹配分数，生成个性化推荐列表。在NJUBlockchain平台提供的数据集上进行的实验验证了模型的有效性。实验结果表明，所提模型在推荐召回率上有显著提高，能够更精准地捕捉研究者的动态兴趣。这一研究不仅提高了科技论文推荐系统的效果，也为理解和预测研究人员兴趣演变提供了新的视角和工具。

关键词: 科技论文推荐, 动态偏好, 知识图谱, 注意嵌入, 自注意力机制

Abstract: Scientific paper recommendation systems serve as an effective solution to the problem of information overload in academic databases. This study proposes a knowledge-graph-based method employing attention embeddings for the task of scientific paper recommendation to enhance the effectiveness of recommendations. This method initially constructs a collaborative knowledge graph to integrate user behavior with paper attribute information and optimizes node vector representations using the TransR approach. Subsequently, it introduces an attention sequence module that employs an attention propagation mechanism to learn node features and utilizes a sequence attention mechanism to capture the temporal preferences of users from their reading sequences. Finally, the model calculates match scores between researchers and candidate papers to generate personalized recommendation lists. Experiments conducted on a dataset provided by the "Blockchain Laboratory" have validated the effectiveness of the model. Experimental results indicate that the proposed model significantly improves recommendation recall rates, capturing the dynamic interests of researchers more accurately. This study not only enhances the performance of scientific paper recommendation systems but also provides new perspectives and tools for understanding and predicting the evolution of researcher interests.

Key words: Scientific papers recommendation, Dynamic preferences, Knowledge graph, Attention embedding, Self-attention mechanisms

中图分类号: 

G252.8

网址：面向用户动态偏好的科技论文推荐：一种基于注意嵌入的知识图谱方法 https://mxgxt.com/news/view/1927379

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