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Research and Analysis of Approaches to Recommender Systems Design and Development

Student: Ilyashenko Grigoriy

Supervisor: Olga A. Tsukanova

Faculty: Graduate School of Business

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2019

This thesis is devoted to research of approaches to recommender systems design and development. Basic concepts such as users, items, ratings, ratings matrix are considered. Methods and metrics of quality assessment of recommender systems are shown. The analysis and comparison of various recommender systems design approaches: Non-Personalized, Collaborative Filtering, Content-based, Knowledge-based, Demographic-based, Trust-based, Context-aware, Session-based, Hybrid, is given. Basic mathematical algorithms used in the development of recommender systems are examined. A separate chapter contains the results of a practical study, in which several recommender algorithms were implemented on a test dataset. A comparative analysis of the results was conducted. The work may be of interest both for the scientific community and for developers of recommender systems.

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