Tao Li, Hao Li*, Sheng Zhong, Yan Kang, Yachuan Zhang, Rongjing Bu, Yang Hu
Journal of New Media, Vol.2, No.1, pp. 21-30, 2020, DOI:10.32604/jnm.2020.09767
- 14 August 2020
Abstract In view of the low interpretability of existing collaborative filtering
recommendation algorithms and the difficulty of extracting information from
content-based recommendation algorithms, we propose an efficient KGRS model.
KGRS first obtains reasoning paths of knowledge graph and embeds the entities of
paths into vectors based on knowledge representation learning TransD algorithm,
then uses LSTM and soft attention mechanism to capture the semantic of each path
reasoning, then uses convolution operation and pooling operation to distinguish the
importance of different paths reasoning. Finally, through the full connection layer
and sigmoid function to get the prediction ratings, More >
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