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- W4210846605 abstract "In recent years, the improvement of people’s live standard lead to an increasing demand for travelling, but the information on scenic spots on the Internet is ponderous and the accuracy of scenic spot recommendations is not high. This study aims to solve these problems to realize a more accurate scenic spot recommendation for self-drivings and provide a better visualization of recommendation results. A recommendation model based on the Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation (MKR) algorithm is designed in this study. Then, based on this model, an intelligent knowledge graph based recommendation system for scenic spots is constructed. Rich unstructured text data of scenic spots in major scenic spot websites are crawled using Selenium and are stored and managed by the neo4j graph database. Through experimental simulation, the recommendation accuracy can reach over 84%. Compared to the Propagating User Preferences on the Knowledge Graph for Recommender Systems (RippleNet), the accuracy rate is enhanced by 6.2%." @default.
- W4210846605 created "2022-02-09" @default.
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- W4210846605 date "2021-12-10" @default.
- W4210846605 modified "2023-10-01" @default.
- W4210846605 title "A Travelling Recommendation System based on Knowledge Graph" @default.
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- W4210846605 doi "https://doi.org/10.1109/iaecst54258.2021.9695769" @default.
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