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Novel Recommendation System for Tourist Spots Based on Hierarchical Sampling Statistics and SVD++

机译：基于分层采样统计和SVD ++的旅游点建议制度

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摘要

Recommendation system for tourist spots has very high potential value including social and economic benefits. The traditional clustering algorithms were usually used to build a recommendation system. However, clustering algorithms have the risk on falling into local minimums, which may decrease the final recommendation performance heavily. Few works focused their research on tourist spots recommendation and few recommendation systems consider the population attributes information for fitting the user implicit preference. To address the problem, we focused our research work on designing a novel recommendation system for tourist spots. First a new dataset named "Smart Travel" is created for the following experiments. Then hierarchical sampling statistics (HSS) model is used to acquire the user preference for different population attributes. A new recommendation list named L-A is generated in turn by fitting the excavated the user preference. More importantly, SVD++ algorithm rather than those traditional clustering algorithms is used to predict the user ratings. And a new recommendation list named L-B is generated in turn on the basis of rating predictions. Finally, the two lists L-A and L-B are fused together to boost the final recommendation performance. Experimental results demonstrate that the mean precision, mean recall, and mean F1 values of the proposed recommendation system improve about 7.5%, 6.2%, and 6.5%, respectively, compared to the best competitor. The novel recommendation system is especially better at recommending a group of tourist spots, which means it has higher practical value.
机译：旅游景点推荐系统具有很高的潜在价值，包括社会和经济效益。传统的聚类算法通常用于构建推荐系统。然而，聚类算法具有落入当地最低限额的风险，这可能会严重降低最终推荐性能。很少有效的作品专注于他们对旅游点的研究建议和少数推荐系统考虑拟合用户隐含偏好的人口属性信息。为了解决问题，我们将我们的研究工作致力于为设计旅游景点的新推荐制度。首先为以下实验创建名为“智能旅行”的新数据集。然后，分层采样统计信息（HSS）模型用于获取对不同群体属性的用户偏好。通过拟合挖掘用户首选项，生成名为L-A的新推荐列表。更重要的是，SVD ++算法而不是那些传统的聚类算法用于预测用户评级。并且，命名为L-B的新推荐列表是根据评级预测的基础生成的。最后，两个列表L-A和L-B融合在一起，以提高最终推荐性能。实验结果表明，与最佳竞争对手相比，拟议推荐系统的平均精度，平均召回和平均F1值分别提高了约7.5％，6.2％和6.5％。新颖的推荐系统尤其更好地推荐一组旅游点，这意味着它具有更高的实用价值。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第16期|2072375.1-2072375.15|共15页
  • 作者

    Li Guangli; Hua Jin; Yuan Tian; Wu Jinpeng; Jiang Ziliang; Zhang Hongbin; Li Tao;

  • 作者单位

    East China Jiaotong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China;

    East China Jiaotong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China;

    East China Jiaotong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China;

    East China Jiaotong Univ Software Sch Nanchang Jiangxi Peoples R China;

    East China Jiaotong Univ Software Sch Nanchang Jiangxi Peoples R China;

    East China Jiaotong Univ Software Sch Nanchang Jiangxi Peoples R China;

    Florida Int Univ Sch Comp & Informat Sci Miami FL 33199 USA;

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