首页>
外国专利>
LEARNING GRAPH REPRESENTATIONS USING HIERARCHICAL TRANSFORMERS FOR CONTENT RECOMMENDATION
LEARNING GRAPH REPRESENTATIONS USING HIERARCHICAL TRANSFORMERS FOR CONTENT RECOMMENDATION
展开▼
机译:利用层次变换器学习图形表示法进行内容推荐
展开▼
页面导航
摘要
著录项
相似文献
摘要
Knowledge graphs can greatly improve the quality of content recommendation systems. There is a broad variety of knowledge graphs in the domain including clicked user-ad graphs, clicked query-ad graphs, keyword-display URL graphs etc. A hierarchical Transformer model learns entity embeddings in knowledge graphs. The model consists of two different Transformer blocks where the bottom block generates relation-dependent embeddings for the source entity and its neighbors, and the top block aggregates the outputs from the bottom block to produce the target entity embedding. To balance the information from contextual entities and the source entity itself, a masked entity model (MEM) task is combined with a link prediction task in model training.
展开▼