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Title:Collaborative ranking from ordinal data
Author(s):Thekumparampil, Kiran Koshy
Advisor(s):Oh, Sewoong
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Collaborative ranking
Recommendation system
Revenue management
Ordinal (comparison) data
Multinomial logit (MNL) model
Convex relaxation
Nuclear norm minimization
Abstract:Personalized recommendation systems have to predict preferences of a user for items that have not seen by the user. For cardinal (ratings) data, personalized preference prediction has been efficiently solved over the past few years using matrix factorization related techniques. Recent studies have shown that ordinal (comparison) data can outperform cardinal data in learning preferences, but there has not been much study on learning personalized preferences from ordinal data. This thesis presents a matrix factorization inspired, convex relaxation algorithm to collaboratively learn hidden preferences of users through the multinomial logit (MNL) model, a discrete choice model. It also shows that the algorithm is efficient in terms of the number of observations needed.
Issue Date:2017-04-27
Rights Information:Copyright 2017 Kiran Koshy Thekumparampil
Date Available in IDEALS:2017-08-10
Date Deposited:2017-05

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