基于知识图谱的用户偏好神经建模框架

Neural User Preference Modeling Framework Based on Knowledge Graph

针对目前基于特征和基于路径的知识图谱感知推荐方法的不足,文中提出端到端的将知识图谱引入推荐系统的用户偏好神经建模框架(NUPM).该框架以用户在知识图谱中的历史访问项目为偏好起点,通过知识图谱中实体间的关系链接传播用户偏好,学习用户的潜在偏好,同时使用注意力网络融合各传播阶段偏好特征以构建最终的用户偏好向量.在真实数据集上的对比实验表明文中框架在个性化推荐中对用户偏好刻画的有效性.

An end-to-end neural user preference modeling framework incorporating knowledge graph into recommender systems, neural user preference modeling framework based on knowledge graph(NUPM), is proposed aiming at the limitations of the current feature-based and path-based knowledge aware recommendation method. Historical interaction items of users in knowledge graph are considered as preference origin of NUPM. Then, potential preferences of users are learned by propagating user interests through relational links between entities in knowledge graph. Furthermore, an attention network is exploited to combine the preference features of different propagation stages to construct final user preference vector. The experimental results on real dataset show the effectiveness of NUPM in personalized recommendation for characterizing user preference.