Social navigation of food recipes
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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We performed a 6-month study of a food recommender system to determine the influence of social trails on users choice of recipes. To measure the impact of the recom-mender functionality, we choose to avoid predictive accu-racy metrics, and opted for contextualised subjective meas-ures, comparing recommendations to searching and brows-ing. 18% of the selected recipes came from the list of rec-ommended recipes. In addition, users liked and understood the recommendation functionality.