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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Makar Stetsenko
Methods for Evaluation of Collaborative Filtering Algorithms
7
2017
In this paper we apply an axiomatic approach to study properties of evaluation methods for collaborative filtering algorithms. As a result we formulate five axioms where each axiom captures a property that is believed to be important for an evaluation metric used in a RS field. We study several existing evaluation metrics and show that some of them, such as Precision and NDCG, fail to comply with a number of axioms. Such evaluation metrics theoretically can produce a wrong ordering of RSs under certain circumstances. However, experimental part of this study has shown that popular evaluation metrics generally produce same rankings of well-known collaborative algorithms, such as SVD and k-NN.

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