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    WU Huicong,LI Jiaoe,ZHAO Mingxing,GAO Kai.Point-of-interest recommendation algorithm integrating multiple impact factors[J].Journal of Hebei University of Science and Technology,2020,41(6):500-507
    融合多種影響因子的興趣點推薦算法
    Point-of-interest recommendation algorithm integrating multiple impact factors
    Received:September 10, 2020  Revised:October 16, 2020
    DOI:10.7535/hbkd.2020yx06004
    中文關鍵詞:  自然語言處理  興趣點推薦  地理影響力建模  社會影響力建模  時空影響力建模
    英文關鍵詞:natural language processing  point-of-interest recommendation  geographic influence modeling  social influence modeling  spatial-temporal influence modeling
    基金項目:國家自然科學基金(61772075)
    Author NameAffiliationE-mail
    WU Huicong School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 3067521192@qq.com 
    LI Jiaoe School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang  
    ZHAO Mingxing School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang  
    GAO Kai School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang  
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    中文摘要:
          為了解決興趣點推薦任務中的數據稀疏性問題和充分利用位置社交網絡中的多樣信息提高個性化推薦質量,提出了一種融合多種影響因子的興趣點推薦算法。分別對地理信息和社會信息進行地理影響力建模和社會影響力建模,并聯合時間信息和地理信息進行時空影響力建模,然后以加權求和的方式整合3種影響力評分得到用戶偏好分數,根據用戶偏好分數為每個用戶提供1個包含Top-N[WT]個興趣點的推薦列表。實驗結果顯示,在2個公開數據集上,融合多種影響因子的興趣點推薦模型的性能優于對比模型。地理-社會-時空影響是興趣點推薦任務中的關鍵,對這3種影響建?蔀槿诤详P鍵信息的興趣點推薦研究提供參考。
    英文摘要:
          In order to solve the problem of data sparseness in the task of point-of-interest recommendation and make full use of the diverse information in the location-based social network to further improve the quality of personalized recommendation, a point-of-interest recommendation algorithm integrating multiple impact factors was proposed. Geographic influence modeling and social influence modeling were performed on geographic information and social information, and temporal information and geographic information were combined to model temporal and spatial influence, and the three influence scores were integrated in a weighted summation manner to obtain user preference score. According to the user preference score, each user was provided with a recommendation list containing Top-N points of interest. The experimental results show that on the two public datasets, the point-of-interest recommendation model that integrates multiple impact factors performs better than the baselines. In addition to the user check-in frequency, the geographic-social-spatial-temporal influence is also a key part of the point-of-interest recommendation task, and the modeling of these three influences is of great significance, which provides certain reference value for the research of point-of-interest recommendation integrating key information.
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