A Survey of Recommendation Systems and Performance Enhancing Methods

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Julkaisun nimi: A Survey of Recommendation Systems and Performance Enhancing Methods
Tekijä: Liao, Ke
Muu tekijä: Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta
Julkaisija: Helsingin yliopisto
Päiväys: 2020
Kieli: eng
URI: http://urn.fi/URN:NBN:fi:hulib-202001271156
Opinnäytteen taso: pro gradu -tutkielmat
Oppiaine: Tietojenkäsittelytiede
Tiivistelmä: With the development of web services like E-commerce, job hunting websites, movie websites, recommendation system plays a more and more importance role in helping users finding their potential interests among the overloading information. There are a great number of researches available in this field, which leads to various recommendation approaches to choose from when researchers try to implement their recommendation systems. This paper gives a systematic literature review of recommendation systems where the sources are extracted from Scopus. The research problem to address, similarity metrics used, proposed method and evaluation metrics used are the focus of summary of these papers. In spite of the methodology used in traditional recommendation systems, how additional performance enhancement methods like machine learning methods, matrix factorization techniques and big data tools are applied in several papers are also introduced. Through reading this paper, researchers are able to understand what are the existing types of recommendation systems, what is the general process of recommendation systems, how the performance enhancement methods can be used to improve the system's performance. Therefore, they can choose a recommendation system which interests them for either implementation or research purpose.


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