Recommender systems apply machine learning methods to solve the task of providing appropriate suggestions to users in both static and dynamic environments. An example of this is a movie service like Netflix that recommends movies to its users. Although many algorithms have been proposed, making predictions for users with few ratings remains a challenge in recommender systems. In this study the performance of the algorithm k-NN, both user- and item-based, was empirically evaluated. This was done using the MovieLens 1M and 100K datasets in scenarios where the users have between 1 and 9 ratings, simulating cold-start scenarios of various degree. The results were then compared with the accuracy of the algorithm in a simulated normal case, to see how the cold-start affected the two algorithms, and which one of them that handled it best. In summary, this report shows that user-based k-NN performs better in relation to item-based k-NN for new users having few rated items. Overall the accuracy improved as the number of ratings increased for the new users for both user- and item-based k-NN.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-208661 |
| Date | January 2017 |
| Creators | Lorentz, Robert, Ek, Oskar |
| Publisher | KTH, Skolan för datavetenskap och kommunikation (CSC) |
| 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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