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数据分析与知识发现  2018, Vol. 2 Issue (7): 63-71     https://doi.org/10.11925/infotech.2096-3467.2018.0179
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于不确定近邻的旅游产品协同过滤推荐算法研究*
赵雅楠1(), 王育清2
1同济大学经济与管理学院 上海 200092
2上海理工大学经济与管理学院 上海 200093
Research on Collaborative Filtering Traveling Products Recommendation Algorithm Based on IUNCF
Zhao Ya’nan1(), Wang Yuqing2
1School of Economics and Management, Tongji University, Shanghai 200092, China
2School of Economics and Management, University of Shanghai for Science and Technology, Shanghai 200093, China
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摘要 

目的】解决传统协同推荐技术在智慧旅游产业运用中的数据稀疏性、冷启动等问题。【方法】结合基于用户和基于内容的协同推荐技术, 对用户进行K-means聚类后动态分类筛选, 为推荐类型分配权重, 提出基于不确定近邻的旅游产品协同过滤推荐算法IUNCF。【结果】基于不同相似性阈值和推荐数目对真实旅游数据进行算法检验, 实验结果表明, IUNCF算法的MAE值和F指标分别达到0.243和0.764, IUNCF可提高旅游产品推荐的准确度和有效性。【局限】IUNCF算法应针对现阶段消费低频性等特点进一步优化, 并扩展运用范围。【结论】IUNCF算法在对用户精准推荐智慧旅游产品领域具有较高价值。

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赵雅楠
王育清
关键词 旅游推荐不确定近邻相似性阈值协同推荐    
Abstract

[Objective] This paper tries to address the challenges facing Smart Tourism industry, such as data sparseness and cold start, with the help of collaborative recommendation technology. [Methods] First, we clustered users with the K-means algorithm and then filtered and classified them dynamically based on the combination of collaborative recommendation technology. Then, we assigned weight to the recommended types and proposed a new algorithm based on Improved Uncertain Neighbors Collaborative Filtering (IUNCF). Finally, we examined the proposed algorithm with real world tourism data of different similarity thresholds and recommended numbers. [Results] The MAE value and F-measure reached 0.243 and 0.764, which showed the effectiveness of IUNCF in accuracy and reliability. [Limitations] The IUNCF algorithm needs to be further optimized to deal with the low frequency consumption issue. We could also extend the application of this new model. [Conclusions] The proposed IUNCF algorithm could precisely recommend smart tourism products to the consumers.

Key wordsTravel Recommendations    Uncertain Neighbors    Similarity Threshold    Collaborative Recommendation
收稿日期: 2018-02-11      出版日期: 2018-08-15
ZTFLH:  TP393  
基金资助:*本文系同济大学研究生教育研究与改革项目“大数据时代背景下基于翻转课堂的教学改革研究——以公共政策为例”(项目编号: 1200104162)的研究成果之一
引用本文:   
赵雅楠, 王育清. 基于不确定近邻的旅游产品协同过滤推荐算法研究*[J]. 数据分析与知识发现, 2018, 2(7): 63-71.
Zhao Ya’nan,Wang Yuqing. Research on Collaborative Filtering Traveling Products Recommendation Algorithm Based on IUNCF. Data Analysis and Knowledge Discovery, 2018, 2(7): 63-71.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0179      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I7/63
  IUNCF算法过程及框架
I1 I2 I3 I4 I5
u 4 5 2 3 4
v 5 4
  对于用户u, 用户v没有推荐能力
I1 I2 I3 I4 I5
u 4 5 2 0 4
v 4 5 2 1 4
  对于用户u, 用户v没有推荐能力
I1 I2 I3 I4 I5
u 4 3 2 0 4
v 4 3 2 5 4
  对于用户u, 用户v有推荐能力
  原始实验数据格式
  相似性阈值对MAE的影响
  TOP-N取值的不同对F值的影响
阈值
指标
0.4 0.5 0.6 0.7 0.8 0.85 0.9 0.95
准确率 0.2743 0.2765 0.2832 0.2779 0.3276 0.3335 0.3452 0.3585
召回率 0.2284 0.2304 0.2357 0.2266 0.2972 0.3031 0.3189 0.3377
F值 0.6865 0.6912 0.7093 0.7185 0.7299 0.7413 0.7527 0.7641
  IUNCF算法下不同相似性阈值的各项指标(Top-N=20)
阈值
指标
0.4 0.5 0.6 0.7 0.8 0.85 0.9 0.95
准确率 0.2524 0.2635 0.2678 0.2702 0.2779 0.2837 0.2894 0.2952
召回率 0.2027 0.2183 0.2257 0.2239 0.2360 0.2436 0.2461 0.2519
F值 0.6687 0.6646 0.6583 0.6812 0.6760 0.6791 0.7026 0.7128
  IUNCF算法下不同相似性阈值的各项指标(Top-N=30)
  IUNCF与KCF推荐结果MAE值比较
  IUNCF与KCF推荐结果的F值比较
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