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Efficient Global Correlation Measures for a Collaborative Filtering Dataset (CROSBI ID 248493)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Kurdija, Adrian Satja ; Šilić, Marin ; Vladimir, Klemo ; Delač, Goran Efficient Global Correlation Measures for a Collaborative Filtering Dataset // Knowledge-based systems, 147 (2018), 1; 36-42. doi: 10.1016/j.knosys.2018.02.013

Podaci o odgovornosti

Kurdija, Adrian Satja ; Šilić, Marin ; Vladimir, Klemo ; Delač, Goran

engleski

Efficient Global Correlation Measures for a Collaborative Filtering Dataset

Recommender systems based on collaborative filtering (CF) rely on datasets containing users’ taste preferences for various items. Accuracy of various prediction approaches depends on the amount of similarity between users and items in a dataset. As a heuristic estimate of this data quality aspect, which could serve as an indicator of the prediction ability, we define the Global User Correlation Measure (GUCM) and the Global Item Correlation Measure (GICM) of a dataset containing known user-item ratings. The proposed measures range from 0 to 1 and describe the quality of the dataset regarding the user-user and item-item similarities: a higher measure indicates more similar pairs and better prediction ability. The experiments show a correlation between the proposed measures and the accuracy of standard prediction models. The measures can be used to quickly estimate whether a dataset is suitable for collaborative filtering and whether we can expect high prediction accuracy of user-based or item- based CF approaches.

collaborative filtering ; Dataset quality ; Global correlation ; User similarity ; Item similarity

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Podaci o izdanju

147 (1)

2018.

36-42

objavljeno

0950-7051

1872-7409

10.1016/j.knosys.2018.02.013

Povezanost rada

Računarstvo

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