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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (5): 92-104    DOI: 10.11925/infotech.2096-3467.2019.1080
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Recommending Tourism Attractions Based on Segmented User Groups and Time Contexts
Zheng Songyin,Tan Guoxin(),Shi Zhongchao
National Research Center of Cultural Industries, Central China Normal University, Wuhan 430079, China
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[Objective] This study tries to provide personalized recommendations for tourists, aiming to improve the low efficiency of user decision-making due to information overload.[Methods] We proposed a new SPT (user Similarity, Popular spot and Time) algorithm, and used real data from Ctrip to compare its recommendation results with traditional algorithms. We also proposed a method to construct training set based on “segmented user groups” and examined its impacts on the recommendation results.[Results] The SPT algorithm yielded better results than traditional recommendation methods in precision, recall, coverage and popularity. The algorithm based on “segmented user groups” further improved the effectiveness of recommendation. The precision and recall of the proposed algorithm reached 43.75% and 61.59%.[Limitations] The algorithm could not find similar users for new users. Our new method requires further testing with more datasets.[Conclusions] The proposed method improves recommendation results of tourism attractions, as well as tourists’ decision-making and personalized services.

Key wordsAttractions Recommendation      Segmented User Groups      Time Contexts      Collaborative Filtering      Personalized Tourism     
Received: 27 September 2019      Published: 15 June 2020
ZTFLH:  TP391 G35  
Corresponding Authors: Tan Guoxin     E-mail:

Cite this article:

Zheng Songyin,Tan Guoxin,Shi Zhongchao. Recommending Tourism Attractions Based on Segmented User Groups and Time Contexts. Data Analysis and Knowledge Discovery, 2020, 4(5): 92-104.

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Tourist Attractions Recommendation Model Based on Segmented User Group and Time Context
No. userId attractions content date score
1 奥拉尔维沃宋玉 武汉大学 可以趁櫻花季的時候去看… 2019-07-19 4
2 出去玩呢 武汉大学 武汉大学确实很大… 2019-07-19 4
3 奥拉尔维沃宋玉 武汉大学 春天的櫻花很美很漂亮… 2019-07-19 4
45504 DOSMO 武汉玛雅海滩水公园 亲们去玛雅一定得自带拖鞋… 2013-07-13 4
45505 DOSMO 武汉玛雅海滩水公园 总体说这次游玩还是不错的… 2013-07-13 5
Original Dataset
No. userId attractions date score
1 奥拉尔维沃宋玉 武汉大学 07 4
2 出去玩呢 武汉大学 07 4
3 奥拉尔维沃宋玉 武汉大学 07 4
45504 DOSMO 武汉玛雅海滩水公园 07 4
45505 DOSMO 武汉玛雅海滩水公园 07 5
Original Dataset After Preliminary Processing
No. userId attractions month score num
1 300****030 武汉大学 3 5 43
2 300****030 黄鹤楼 5 5 43
3 300****030 武汉欢乐谷 2 5 43
39438 **2345kk 东湖游船 10 4 1
39439 **-游-客 木兰山 10 5 1
Experimental Dataset After Preprocessing
月份 评论数占比(%) 月份 评论数占比(%)
1月 4.47 7月 8.67
2月 6.35 8月 8.48
3月 9.82 9月 7.40
4月 11.73 10月 12.49
5月 11.17 11月 6.04
6月 8.37 12月 5.00
Proportion of Reviews in Different Months
Popularity of Some Attractions in Different Months
Popularity of Some Attractions Among Segmented User Group
推荐算法 准确率(%) 召回率(%) 覆盖率(%) 平均流行度
Random 19.41 27.33 100.00 3.059 377
Popular 41.99 59.11 58.82 3.866 054
ScoreCF 25.86 26.60 98.04 3.357 215
UserCF 42.72 60.14 62.75 3.848 093
UserIIF 42.94 60.46 60.78 3.854 983
SPT 43.38 61.08 64.71 3.831 839
Performance Comparison of Different Recommendation Algorithms (Top10)
Top-N 准确率(%) 召回率(%) 覆盖率(%) 平均流行度
5 54.85 38.61 49.02 3.993 657
10 43.38 61.08 64.71 3.831 839
15 35.29 74.53 80.39 3.707 977
20 29.41 82.82 92.16 3.576 896
25 28.20 78.88 100.00 3.421 675
Performance of SPT Algorithm with Different Top-N
Performance of Different Algorithms Under Segmented User Group (Top10)
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