Review and Discussion of Personalised News Recommendation Systems

doi:10.3969/j.issn.1006-2475.2024.04.003

Abstract

Abstract:
Abstract： With the rapid development of news media technology and the exponential growth of the number of online news， personalised news recommendation plays an extremely crucial role in order to solve the problem of online information overload. It learns users' browsing behaviour， interests and other information， and actively provides user with news of interest， thus improving user's reading experience. Personalised news recommendation has become a hot research and practical problem in the field of journalism and computer science， and experts in the industry have proposed various recommendation algorithms to improve the performance of recommendation systems. In this paper， we systematically describe the latest research status and progress of personalised news recommendation. firstly， we briefly introduce the architecture of news recommendation systems， and then we study the key recommendation algorithms and common evaluation metrics in news recommendation systems. Although personalised news recommendation brings a good experience to users， it also brings a lot of unknown effects to users. Unlike other news recommendation reviews， this paper also examines the impact of current news recommendation systems on user behaviour and the problems they face. Finally， the paper proposes research directions and future work on personalised news recommendation based on the current problems encountered.

Key words: Key words： recommendation system, user personalization, news recommendation, user behavior

CLC Number:

TP391

ZHAI Mei. Review and Discussion of Personalised News Recommendation Systems[J]. Computer and Modernization, 2024, 0(04): 12-20.

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