建筑领域文档推荐模型设计与实现

Design and Implementation of Document Recommendation Model in Architecture Field

在面向建筑领域的文档推荐任务上,为了更好地理解用户偏好,提出了一种多任务学习方法 KGRP(unifying knowledge graph learning and recommendation:for a better user preferences),它将知识图谱嵌入和文档推荐两个任务联合学习。我们为KGRP设计了一个交叉压缩单元,它能够显式地为文档特征和实体特征之间的高阶交互建模,补充文档和实体的信息,让两个任务共享更多的特征信息。在建筑领域的文档数据集上实验结果显示,KGRP算法推荐性能良好,能够根据用户的交互行为与偏好模型推荐合适的文档。

On the task of document recommendation in the architectural field, in order to better understand user preferences, a multi task learning method, KGRP(unifying knowledge graph learning and recommendation: for a better user preferences) is proposed, which embeds the knowledge graph and document recommendation tasks for joint learning. We designed a cross compression unit for KGRP, which can explicitly model the high-level interaction between document features and entity features, supplement the information of documents and entities, and enable the two tasks to share more feature information. The experimental results on the document dataset in the architectural field show that the KGRP algorithm has good recommendation performance, and can recommend appropriate documents according to the user’s interaction behavior and preference model.