@Article{jnm.2020.09767,
AUTHOR = {Tao Li, Hao Li, Sheng Zhong, Yan Kang, Yachuan Zhang, Rongjing Bu, Yang Hu},
TITLE = {Knowledge Graph Representation Reasoning for Recommendation System},
JOURNAL = {Journal of New Media},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {21--30},
URL = {http://www.techscience.com/JNM/v2n1/39838},
ISSN = {2579-0129},
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, and the items are sorted according 
to the prediction ratings to get the user’s recommendation list. KGRS is tested on the 
movielens-100k dataset. Compared with the related representative algorithm, 
including the state-of-the-art interpretable recommendation models RKGE and 
RippleNet, the experimental results show that KGRS has good recommendation 
interpretation and higher recommendation accuracy.},
DOI = {10.32604/jnm.2020.09767}
}