Abstract
The development of academic social network has changed the way researchers make access to academic news. In this paper, we propose a news recommendation system to provide user personalized recommendation based on their profile. Our system presents the generation of user profile: (1) extracts the user tag through TextRank; (2) incorporates the timeliness of the news and generates the user profile according to the users’ behavior; (3) combines the semantic space vector of news, calculate the similarity of the above to make news recommendations. Finally, the experiment is carried out on the academic social network platform - scholat.com. The experimental results show that the news recommendation system can update the user profile in real time, and this recommendation achieves the due goal.
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Acknowledgement
This work was partially supported by Guangzhou Science and Technology Project (No. 201604046017).
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Li, W., Tang, Y., Chen, G., Xiao, D., Yuan, C. (2018). Implementation of Academic News Recommendation System Based on User Profile and Message Semantics. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_46
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DOI: https://doi.org/10.1007/978-981-13-1648-7_46
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