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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (5): 94-104    DOI: 10.11925/infotech.2096-3467.2017.1009
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Recommending Diversified News Based on User’s Locations
Hua Lingfeng1, Yang Gaoming1(), Wang Xiujun2
1School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
2School of Computer Science and Technology, Anhui University of Technology, Maanshan 243032, China
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Abstract  

[Objective] Location-based hybrid recommendation methods are not accurate and have cold-start problem of the existing users in new locations, because they do not incorporate the location information of users well into their design. This paper proposes the Diversity news Location-oriented Recommendation algorithm (DLR), aiming to improve the performance of traditional methods. [Methods] First, we clustered the location tags from users’ historical behavior data. Then, we used the LDA model and the classic collaborative filtering algorithm based on 3D similarity to establish a preference model for each position cluster. Finally, we obtained a user’s current position with the help of GPS, and selected a preference cluster model for this user. [Results] The proposed method generated two preference lists, and chose the Top-n of the two lists as recommended news for the user. [Limitations] The proposed method could not effectively solve the cold start issue facing new users. [Conclusions] The DLR model could improve the diversity and accuracy of recommended news.

Key wordsNews Recommendation      User Similarity      Location Based Service      Collaborative Filtering     
Received: 09 October 2017      Published: 20 June 2018
ZTFLH:  TP181  

Cite this article:

Hua Lingfeng,Yang Gaoming,Wang Xiujun. Recommending Diversified News Based on User’s Locations. Data Analysis and Knowledge Discovery, 2018, 2(5): 94-104.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1009     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I5/94

软\硬件 配置
操作系统 Win 7旗舰版
Hardware CPU 3.4GHz、4GB、1TB
Software Python 3.5 64bit
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