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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Vitaliy Don
Recommendation of Relevant News Resources Based in Machine Learning Techniques
Data Science
(Master’s programme)
2018
In this thesis work analyzes the methods of recommendations of news resources.

News quickly out of date. The purpose of the work is to apply existing reference

models in dataset of news resources and assess the quality of their work.

The following algorithms were used to recommend the news: PureSVD,

ImplicitALS, LightFMWrapper, PopularityModel, RandomModel. To measure

the quality were used: F-measure, precision, recall, nDCG (Normalized Discounted

Cumulative Gain).

Results of research: ImplicitALS was the best, followed by PureSVD,

PopularityModel, LightFMWrapper, RandomModel.

There were also tests on real users, where recommendations were displayed

in a special recommendation block. The quality metric was ctr (the ratio of the

number of clicks on an article to its impressions in the recommendation block).

Results of tests: PureSVD was the best, followed by ImplicitALS,

PopularityModel, LightFMWrapper, RandomModel.

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