JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (7): 1-10.doi: 10.6040/j.issn.1671-9352.0.2021.151

   

Personalized hotel recommendation algorithm based on user implicit data

SHI Da1, YU Miao-chuan2*, LI Meng-qi2   

  1. 1. School of Tourism and Hospitality Management, Dongbei University of Finance and Economics, Dalian 116025, Liaoning, China;
    2. International Business College, Dongbei University of Finance and Economics, Dalian 116025, Liaoning, China
  • Published:2021-07-19

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Abstract: Based on collaborative filtering of items, this paper mines and models the implicit feedback data, designs the implicit feedback preference scoring rules, and gives a new definition to calculate the hotel similarity formula. At the same time, considering the basic characteristics of users will also have an impact on users personalized needs and the limitations of a single algorithm, this paper further introduces the XGBoost model, and filters the improved recommendation results with XGBoost training, so as to obtain a better personalized hotel recommendation system. This paper adopts real desensitization data to prove that the recommendation effect of building personalized hotel recommendation system based on cascade model is more accurate, which has a strong reference value for the personalized service of online hotel platform.

Key words: personalized hotel recommendation, collaborative filtering, implicit feedback preference design, XGBoost model, cascading model

CLC Number: 

  • N94-0
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