Recently, researchers utilize Knowledge Graph (KG) as side information in recommendation system to address cold start and sparsity issue and improve the recommendation performance. Existing KG-aware recommendation model use the feature of neighboring entities and structural information to update the embedding of currently located entity. Although the fruitful information is beneficial to the following task, the cost of exploring the entire graph is massive and impractical. In order to reduce the computational cost and maintain the pattern of extracting features, KG-aware recommendation model usually utilize fixed-size and random set of neighbors rather than complete information in KG. Nonetheless, there are two critical issues in these approaches: First of all, fixed-size and randomly selected neighbors restrict the view of graph. In addition, as the order of graph feature increases, the growth of parameter dimensionality of the model may lead the training process hard to converge. To solve the aforementioned limitations, we propose GraphSW, a strategy based on stage-wise training framework which would only access to a subset of the entities in KG in every stage. During the following stages, the learned embedding from previous stages is provided to the network in the next stage and the model can learn the information gradually from the KG. We apply stage-wise training on two SOTA recommendation models, RippleNet and Knowledge Graph Convolutional Networks (KGCN). Moreover, we evaluate the performance on six real world datasets, Last.FM 2011, Book-Crossing,movie, LFM-1b 2015, Amazon-book and Yelp 2018. The result of our experiments shows that proposed strategy can help both models to collect more information from the KG and improve the performance. Furthermore, it is observed that GraphSW can assist KGCN to converge effectively in high-order graph feature.
翻译:最近,研究人员利用知识图(KG)作为建议系统中的侧边信息,以解决冷开始和偏僻问题,并改进建议性能。现有的KG-aware建议模式使用邻近实体的特征和结构信息更新当前定位实体的嵌入。虽然丰富的信息有益于以下任务,但对整个图表进行探索的成本是巨大和不切实际的。为了降低计算成本并保持提取功能的格局,KG-aware建议模式通常使用固定规模和随机的邻居集成,而不是KG的完整信息。然而,这些方法中有两个关键问题:首先,固定规模和随机选择的邻居模式使用邻近实体的特征和结构信息来更新当前定位实体的嵌入。此外,随着图形特性的顺序的提高,该模型的参数的维度增长可能会导致培训过程难以趋同。为了解决上述局限性,我们提议了一个基于阶段性培训框架的图形SGSWW,该框架只能在每个阶段访问KG的一组实体。在接下来的阶段,从以前的阶段向网络提供的是固定规模和随机选择的图像网络。我们从下一个阶段提供的SFM-IFS-S-RO的运行的运行结果显示,我们从最后的版本的运行的运行的运行的运行的运行的运行结果数据。我们从最后的版本的运行中可以学习到最新的版本的版本的版本的版本的版本的运行中学习。