Investigations into user rating information and predictive accuracy in a collaborative filtering domain

Authors:
Josephine Griffith;Colm O'Riordan;Humphrey Sorensen
Affiliations:
National University of Ireland, Galway;National University of Ireland, Galway;University College Cork, Ireland
Venue:
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Year:
2012

Citing 14
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Abstract

The work described in this paper extracts user rating information from collaborative filtering datasets, and for each dataset uses a supervised machine learning approach to identify if there is an underlying relationship between rating information in the dataset and the expected accuracy of recommendations returned by the system. The underlying relationship is represented by decision tree rules. The rules can be used to indicate the predictive accuracy of the system for users of the system. Thus a user can know in advance of recommendation the level of accuracy to expect from the collaborative filtering system and may have more (or less) confidence in the recommendations produced. The experiment outlined in this paper aims to test the accuracy of the rules produced using three different datasets. Results show good accuracy can be found for all three datasets.