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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.
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Received: 09 October 2017
Published: 20 June 2018
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