ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Tag-based personalized travel recommendation

  • School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.07.002
  • Received Date: 28 August 2016
  • Rev Recd Date: 08 December 2016
  • Publish Date: 31 July 2017
    Abstract

  • Abstract

    The disparity between the huge number of tourist attraction and the limited number of trigs made by tourists has resulted in the sparseness of tourist travel data, which seriously affects the accuracy of the recommendation results. To solve this problem, four kinds of tags area, time, topic, type were extracted from a mass of travel notes to enrich the data. On the one hand, travel attractions were recommended to users by tag-content-based recommendation algorithm. On the other hand, user interest features were described with attractions feature tags. Then, similar users were found according to the interest tags of users and attractions were recommended through collaborative filtering. The tag-based collaborative filtering algorithm by 63.7% compared with the collaborative filtering recommendation algorithm and by 22.5% compared with the attraction-heat-based recommendation algorithm. Tag-content-based recommendation algorithm can improve the accuracy by 27.6% compared with the attraction-heat-based recommendation algorithm. The two algorithms were further combined with linear weight so that the two algorithms complement each other, resulting in better recommendation results. Our tag-based hybrid algorithm can make a significant improvement, i.e. increasing the accuracy by 61.3% over the tag-based collaborative filtering algorithm and 54.7% over the tag-content-based recommendation algorithm. The improvement of recommendation accuracy will enhance the user experience and make online travel websites more competitive.

    Abstract

    The disparity between the huge number of tourist attraction and the limited number of trigs made by tourists has resulted in the sparseness of tourist travel data, which seriously affects the accuracy of the recommendation results. To solve this problem, four kinds of tags area, time, topic, type were extracted from a mass of travel notes to enrich the data. On the one hand, travel attractions were recommended to users by tag-content-based recommendation algorithm. On the other hand, user interest features were described with attractions feature tags. Then, similar users were found according to the interest tags of users and attractions were recommended through collaborative filtering. The tag-based collaborative filtering algorithm by 63.7% compared with the collaborative filtering recommendation algorithm and by 22.5% compared with the attraction-heat-based recommendation algorithm. Tag-content-based recommendation algorithm can improve the accuracy by 27.6% compared with the attraction-heat-based recommendation algorithm. The two algorithms were further combined with linear weight so that the two algorithms complement each other, resulting in better recommendation results. Our tag-based hybrid algorithm can make a significant improvement, i.e. increasing the accuracy by 61.3% over the tag-based collaborative filtering algorithm and 54.7% over the tag-content-based recommendation algorithm. The improvement of recommendation accuracy will enhance the user experience and make online travel websites more competitive.
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    • References(16)
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    LIU Q, CHEN E H, XIONG H, et al. A cocktail approach for travel package recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2): 278-293.
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    UMYAROV A, TUZHILIN A. Improving collaborative filtering recommendations using external data[C]// 8th International Conference on Data Mining. IEEE Press, 2008: 618-627.
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    聂恩伦,陈黎,王亚强,等. 基于K近邻的新话题热度预测算法[J]. 计算机科学, 2012, 39(6A): 257-260.
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    [1]
    ADOMAVICIUS G, TUZHILIN A and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
    [2]
    GE Y, XIONG H, TUZHILIN A, et al. An energy-efficient mobile recommender system[J]. Knowledge Discovery and Data Mining, 2010, 18(1): 899-908.
    [3]
    KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37.
    [4]
    LINDEN G, SMITH B, YORK J. Amazon.com recommendations: Item-to-item collaborative filtering[J]. IEEE Internet Computing, 2003, 7(1): 76-80.
    [5]
    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.
    [6]
    PAZZANI M J. A framework for collaborative, content-based and demographic filtering[J]. Artificial Intelligence Review, 1999, 13(5-6): 393-408.
    [7]
    KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37.
    [8]
    LIU Q, GE Y, LI Z M, et al. Personalized travel package recommendation[C]// Proceedings of 11th International Conference on Data Mining. Vancouver, Canada: IEEE Computer Society, 2011: 407-416.
    [9]
    BAO J, ZHENG Y, MOKBEL M F. Location-based and preference-aware recommendation using Sparse Geo-Social Networking data[C]// Proceedings of the 20th International Conference on Advances in Geographic Information Systems. Redondo Beach, USA: ACM Press, 2012: 199-208.
    [10]
    QIAN X M, FENG H, ZHAO G S, et al. Personalized recommendation combining user interest and social circle[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(7): 1487-1502.
    [11]
    FENG H, QIAN X M. Mining user-contributed photos for personalized product recommendation[J]. Neurocomputing, 2014, 129: 409-420.
    [12]
    刘树栋,孟祥武. 基于位置的社会化网络推荐系统[J]. 计算机学报, 2015, 38(2): 322-336.
    LIU Shudong, MENG Xiangwu. Rcommender systems in location-based social networks[J]. Chinese Journal of Computers, 2015, 38(2): 322-336.
    [13]
    LIU Q, CHEN E H, XIONG H, et al. A cocktail approach for travel package recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2): 278-293.
    [14]
    UMYAROV A, TUZHILIN A. Improving collaborative filtering recommendations using external data[C]// 8th International Conference on Data Mining. IEEE Press, 2008: 618-627.
    [15]
    聂恩伦,陈黎,王亚强,等. 基于K近邻的新话题热度预测算法[J]. 计算机科学, 2012, 39(6A): 257-260.
    NIE Enlun, CHEN Li, WAGN Yaqiang, et al. Algorithm for prediction of new topic’s hotness using the K-nearest neighbors[J]. Computer Science, 2012, 39(6A): 257-260.
    [16]
    项亮. 推荐系统实践[M]. 北京: 人民邮电出版社, 2012.
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