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A news-topic recommender system based on keywords extraction

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Abstract

In recent years, internet news has become one of the most important channels for information acquisition, as more and more people read news through internet connected computers, tablets, and smart phones, etc. Owing to the constantly reproduced news, the number of online media increases dramatically and the volume of news also expands rapidly. Consequently, obtaining primary information from the internet is of great interest. This paper presents a news-topic recommender system based on keywords extraction. It is shown that the proposed system is very effective in acquiring specific topics within any specific period of time.

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Acknowledgements

This work was supported by Seoul National University Big Data Institute through the Data Science Research Project 2015.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, Gwanak-gu, Seoul, South Korea

    Zihuan Wang & Jong-Mo Seo

  2. Department of Communication, Seoul National University, Gwanak-gu, Seoul, South Korea

    Kyusup Hahn

  3. Department of Linguistics, Seoul National University, Gwanak-gu, Seoul, South Korea

    Youngsam Kim

  4. Big Data Institute, Seoul National University, Gwanak-gu, Seoul, South Korea

    Sanghyup Song

Authors
  1. Zihuan Wang
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  2. Kyusup Hahn
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  3. Youngsam Kim
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  4. Sanghyup Song
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  5. Jong-Mo Seo
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Correspondence to Jong-Mo Seo.

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Wang, Z., Hahn, K., Kim, Y. et al. A news-topic recommender system based on keywords extraction. Multimed Tools Appl 77, 4339–4353 (2018). https://doi.org/10.1007/s11042-017-5513-0

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