融合知识图谱与注意力机制的项目推荐算法OA北大核心CSTPCD

Knowledge graph contains rich semantic information,and is widely used in different recommendation scenarios.However,the existing recommendation method based on knowledge graph considers the relationship between user and project interaction in a coarse-grained way in the modeling process of graph neural network,and ignores the importance of different historical items when constructing user feature models.To solve such problems,the item recommendation algorithm that integrates knowledge graph and attention mechanism is proposed.The algorithm aggregates user character-istics and uses the attention mechanism to learn the high-order latent relationship of knowledge graph.When constructing item features,the algorithm propagates the item embedding representation between their neighborhoods,further integrates the graph convolutional network,and finally uses multi-layer neural networks for prediction.On the two sets of public datasets,the recall rate is improved by up to 6.9%by comparing the baseline algorithm.