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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (7): 63-71    DOI: 10.11925/infotech.2096-3467.2018.0179
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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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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 words： Travel Recommendations      Uncertain Neighbors      Similarity Threshold      Collaborative Recommendation     
Received: 11 February 2018      Published: 15 August 2018
ZTFLH:  TP393  
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Ya’nan Zhao
Yuqing Wang

Cite this article:

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0179     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I7/63

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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
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
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