Collaboratively Learning Preferences from Ordinal Data

NeurIPS 2015 Sewoong OhKiran K. ThekumparampilJiaming Xu

In applications such as recommendation systems and revenue management, it is important to predict preferences on items that have not been seen by a user or predict outcomes of comparisons among those that have never been compared. A popular discrete choice model of multinomial logit model captures the structure of the hidden preferences with a low-rank matrix... (read more)

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