[1]JIA Zhonghao,BIN Chenzhong,GU Tianlong,et al.Personalized attraction recommendation based on the knowledge graph and users’ long-term and short-term preferences[J].CAAI Transactions on Intelligent Systems,2020,15(5):990-997.[doi:10.11992/tis.201904064]
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Personalized attraction recommendation based on the knowledge graph and users’ long-term and short-term preferences

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 990-997 Column: 学术论文—知识工程 Public date: 2020-09-05
Title:
Personalized attraction recommendation based on the knowledge graph and users’ long-term and short-term preferences
Author(s):
JIA Zhonghao;  BIN Chenzhong;  GU Tianlong;  Chang Liang;  Zhu Guiming;  Chen Wei
Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
Keywords:
knowledge graph; recommendation algorithm; network representation learning; gated recurrent unit; personalized attractions recommendation; users’ long-term and short-term preference; feature learning
CLC:
TP301
DOI:
10.11992/tis.201904064
Abstract:
The session-based recommendation algorithm has achieved good results in many fields. However, several problems, such as not considering the relationship between all items, will reduce the recommendation accuracy considerably. Therefore, a personalized attraction recommendation method based on the knowledge graph and users’ long-term and short-term preferences (KG-ULSP) is proposed. The knowledge graph is derived using the network representation learning method and the feature vector representation of the learning attractions. The attractions with similar structure and attribute are close to each other in the low-dimensional space and express high-level semantic features. In addition, the sequence information is modeled by the gated recurrent unit and the access sequence information is further extracted by feature extraction. Moreover, given that the users’ preferences may change with time, the KG-ULSP model learns both long-term and short-term preferences of the user and predicts and returns the list of recommendations that users may be interested in. The validity of the proposed method is verified by experiments on real tourism data.
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