Open Access
ARTICLE
Knowledge Graph Representation Reasoning for Recommendation System
Tao Li, Hao Li*, Sheng Zhong, Yan Kang, Yachuan Zhang, Rongjing Bu, Yang Hu
Department of Software, Yunnan University, Kunming, 650500, China
* Corresponding Author: Hao Li. Email:
Journal of New Media 2020, 2(1), 21-30. https://doi.org/10.32604/jnm.2020.09767
Received 17 January 2020; Accepted 31 January 2020; Issue published 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, 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.
Keywords
Cite This Article
T. Li, H. Li, S. Zhong, Y. Kang, Y. Zhang
et al., "Knowledge graph representation reasoning for recommendation system,"
Journal of New Media, vol. 2, no.1, pp. 21–30, 2020. https://doi.org/10.32604/jnm.2020.09767