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A topic definition model of self-media news based on Louvain algorithm

Published: 17 May 2021 Publication History

Abstract

In order to attract readers, most self-media writers contain keywords that reflect the theme of "net celebrity". However, the title keywords often have a certain deviation from the web page topic. In the news recommendation system, this deviation will affect the accuracy of the recommendation and thus the user experience. This paper studies the problem and proposes a self-media news topic definition model (MNLA) based on Louvain algorithm. First, determine the major topic of the news by analyzing the news headline and body tag words, then obtain the high-quality news page linked to the news, and finally determine the small topic of the web page based on the Louvain community algorithm. According to experiments, compared with other topic definition methods based on text quantity and matrix analysis, the method in this paper has certain accuracy advantages.

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      cover image ACM Other conferences
      CONF-CDS 2021: The 2nd International Conference on Computing and Data Science
      January 2021
      1142 pages
      ISBN:9781450389570
      DOI:10.1145/3448734
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 17 May 2021

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      Author Tags

      1. Louvain algorithm
      2. Self-media news
      3. Theme definition

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