[1]JIA Zhonghao,GU Tianlong,BIN Chenzhong,et al.Tourism knowledge-graph feature learning for attraction recommendations[J].CAAI Transactions on Intelligent Systems,2019,14(3):430-437.[doi:10.11992/tis.201810032]
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Tourism knowledge-graph feature learning for attraction recommendations

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 3 Page number: 430-437 Column: 学术论文—知识工程 Public date: 2019-05-05
Title:
Tourism knowledge-graph feature learning for attraction recommendations
Author(s):
JIA Zhonghao;  GU Tianlong;  BIN Chenzhong;  CHANG Liang;  ZHANG Weitao;  ZHU Guiming
Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
Keywords:
knowledge graph; attribution subgraph; feature learning; neural network; attractions recommendation; network embedding; recommendation algorithm; deep learning
CLC:
TP301
DOI:
10.11992/tis.201810032
Abstract:
The recommendation algorithm based on knowledge graphs has achieved good results in several fields; however, it contains some problems as well. For example, this algorithm cannot effectively extract features from entity relationship tags in the knowledge graph, which reduces its recommendation accuracy. We propose a network embedding method that more fully extracts features from the tourism knowledge graph than the aforementioned method. By independently modeling different label-attribute subgraphs in the tourism knowledge graph and by using a deep learning model to mine node semantic features, such as tourists and scenic spots, we can obtain a feature vector of tourists and attractions that fuses the semantics of various tags. Finally, this method generates a recommended list of scenic spots by calculating the correlation between tourists and scenic spots. Experimental results on a real tourism knowledge graph verify that this network embedding method effectively extracts the deep features of knowledge graph nodes to model the data in the knowledge graph.
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