Customizing Algorithmic Recommendations to Actual and Ideal Preferences

We utilize machine learning algorithms to generate personalized recommendations tailored to people’s actual or ideal preferences. Whereas people are more likely to follow both types of customized recommendations (albeit not equally) over non-customized recommendations, they feel better off and think more highly of the recommendation service when receiving “ideal” recommendations.



Citation:

Poruz Khambatta, Shwetha Mariadassou, Joshua I Morris, and Christian Wheeler (2020) ,"Customizing Algorithmic Recommendations to Actual and Ideal Preferences", in NA - Advances in Consumer Research Volume 48, eds. Jennifer Argo, Tina M. Lowrey, and Hope Jensen Schau, Duluth, MN : Association for Consumer Research, Pages: 994-999.

Authors

Poruz Khambatta, Stanford University, USA
Shwetha Mariadassou, Stanford University, USA
Joshua I Morris, Stanford University, USA
Christian Wheeler, Stanford University, USA



Volume

NA - Advances in Consumer Research Volume 48 | 2020



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