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

PDF (PC)

675

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
[1] 王婧虹,李银胜. 基于人性化用户特征的在线酒店推荐技术研究[J].计算机应用与软件,2019,36(2):113-119. WANG Jinghong, LI Yinsheng. A humanized user features approach for hotel recommendation[J]. Computer Applications and Software, 2019, 36(2):113-119.
[2] 李贵,王爽,李征宇,等. 基于张量分解的个性化标签推荐算法[J]. 计算机科学, 2015, 42(2):267-273. LI Gui, WANG Shuang, LI Zhengyu, et al. Personalized tag recommendation algorithm based on tensor decomposition[J]. Computer Science, 2015, 42(2):267-273.
[3] 王晓耘,赵菁, 徐作宁.基于社会化标注的用户兴趣发现及个性化推荐研究[J].现代情报, 2018, 38(7):67-73. WANG Xiaoyun, ZHAO Jing, XU Zuoning. Research on user interest mining and personalized recommendation based on socialized annotation[J]. Journal of Modern Information, 2018, 38(7):67-73.
[4] 刘平峰, 聂规划, 陈冬林. 电子商务推荐系统研究综述[J]. 情报杂志, 2007(9):46-50. LIU Pingfeng, NIE Guihua, CHEN Donglin. A survey: electronic commerce recommender systems[J]. Journal of Intelligence, 2007(9):46-50.
[5] 许海玲, 吴潇, 李晓东, 等. 互联网推荐系统比较研究[J]. 软件学报, 2009, 20(2):350-362. XU Hailing, WU Xiao, LI Xiaodong, et al. Comparison study of internet recommendation system[J]. Journal of Software, 2009, 20(2):350-362.
[6] XIONG Y N, GENG L X. Research on personalized recommendation model based on dynamic interestingness for online hotel reservation [C] //2010 International Conference on Internet Technology and Applications. Piscataway: IEEE, 2010: 1-5.
[7] 高虎明,李伟丽. 基于协同过滤和 Rankboost 算法的酒店推荐系统[J]. 微计算机信息, 2010, 26(36):206-208. GAO Huming, LI Weili. Hotel recommendation system based on collaborative filtering and Rankboost algorithm[J]. Microcomputer Information, 2010, 26(36):206-208.
[8] 冷亚军,陆青,梁昌勇. 协同过滤推荐技术综述[J].模式识别与人工智能, 2014, 27(8):720-734. LENG Yajun, LU Qing, LIANG Changyong. Survey of recommendation based on collaborative filtering[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(8):720-734.
[9] 邓晓懿,金淳,韩庆平. 基于情境聚类和用户评级的协同过滤推荐模型[J]. 系统工程理论与实践, 2013, 33(11):2945-2953. DENG Xiaoyi, JIN Chun, HAN Qingping. Improved collaborative filtering model based on context clustering and user ranking[J]. Systems Engineering-Theory & Practice, 2013, 33(11):2945-2953.
[10] 孙小华. 协同过滤系统的稀疏性与冷启动问题研究[D].杭州:浙江大学, 2005. SUN Xiaohua. Research of sparsity and cold start problem in collaborative filtering[D]. Hangzhou: Zhejiang University, 2005.
[11] 邓爱林, 朱扬勇, 施伯乐. 基于项目评分预测的协同过滤推荐算法[J]. 软件学报, 2003, 14(9):1621-1628. DENG Ailin, ZHU Yangyong, SHI Bole. A collaborative filtering recommendation algorithm based on item rating prediction[J]. Journal of Software, 2003, 14(9):1621-1628.
[12] DAVIDSON J, LIEBALD B, LIU J, et al. The You Tube video recommendation system[C] //2010 ACM Conference on Recommender Systems. New York: ACM, 2010: 293-296.
[13] SMITH B, LINDEN G. Two decades of recommender systems at Amazon. com[J]. IEEE Internet Computing, 2017, 21(3):12-18.
[14] GOLDBERG D, NIEHOLS D, OKI B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992, 35(12):61-70.
[15] SHARMA Y, BHATT J, MAGON R. A multi-criteria review-based hotel recommendation system[C] //2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. Piscataway: IEEE, 2015: 687-691.
[16] TAKUMA K, YAMAMOTO J, KAMEI S, et al. A hotel recommendation system based on reviews: what do you attach importance to? [C] // Fourth International Symposium on Computing and NETWORKING. Piscataway: IEEE, 2017: 710-712.
[17] 杨辰,刘婷婷, 刘雷, 等. 融合语义和社交特征的电子文献资源推荐方法研究[J]. 情报学报, 2019, 38(6):632-640. YANG Chen, LIU Tingting, LIU Lei, et al. A novel recommendation approach of electronic literature resources combining semantic and social features[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(6):632-640.
[18] 刘建国,周涛,郭强,等.个性化推荐系统评价方法综述[J]. 复杂系统与复杂性科学, 2009,6(3):1-10. LIU Jianguo, ZHOU Tao, GUO Qiang, et al. Overview of the evaluated algorithms for the personal recommendation systems[J]. Complex Systems and Complexity Science, 2009, 6(3):1-10.
[19] 李航. 统计学习方法[M].北京: 清华大学出版社,2012:47-52. Li Hang. Statistical learning methods[M]. Beijing: Tsinghua University Press, 2012: 47-52.
[20] 周志华. 机器学习[M].北京: 清华大学出版社, 2016:225-226. ZHOU Zhihua. Machine learning[M]. Beijing: Tsinghua University Press, 2016: 225-226.
[21] CHEN T, GUESTRIN C. Xgboost: A scalable tree boosting system[C] //Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 785-794.
[22] BREIMAN L. Bagging predictors[J]. Machine Learning, 1996, 24(2):123-140.
[23] BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1):5-32.
[24] GOMEZ-URIBE C A, HUNT N. The netflix recommender system: algorithms, business value, and innovation[J]. ACM Transactions on Management Information Systems(TMIS), 2016, 6(4):1-19.
[1] ZHENG Li-ping, HU Min-jie, YANG Hong-he, LIN Yao-jin. Research on collaborative filtering algorithm based on rough set [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2019, 54(2): 41-50.
[2] YANG Zhen, SI Shu-yong, LI Chao-yang. Information recommendation based on users interest model [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(1): 15-22.
[3] QI Li-li, SUN Jing-yu*, CHEN Jun-jie. Mean model based IBCF algorithm [J]. J4, 2013, 48(11): 105-110.
[4] LIU Jian1, YIN Chun-xia 2*, YUAN Fu-yong3. A collaborative filtering recommendation mechanism based on user profile in unstructured P2P networks [J]. J4, 2011, 46(5): 28-33.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd