One of the most popular methods in recommender systems are matrix factorization (MF) models. In this paper, the sensitivity of sparsity of these models are investigated using a simulation study. Using the MovieLens dataset as a base several dense matrices are created. These dense matrices are then made sparse in two different ways to simulate different kinds of data. The accuracy of MF is then measured on each of the simulated sparse matrices. This shows that the matrix factorization models are sensitive to the degree of information available. For high levels of sparsity the MF performs badly but as the information level increases the accuracy of the models improve, for both samples.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-352653 |
| Date | January 2018 |
| Creators | Strömqvist, Zakris |
| Publisher | Uppsala universitet, Statistiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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