2026-09-18T20:13:35Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/97794 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Oh, Sewoong Thekumparampil, Kiran Koshy 2017-08-10T20:33:28Z 2017-08-10T20:33:28Z 2019-08-11T09:15:35Z 2017-04-27 2017-05 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. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-05-01 The student, Kiran Thekumparampil, accepted the attached license on 2017-04-26 at 15:00. The student, Kiran Thekumparampil, submitted this Thesis for approval on 2017-04-26 at 15:01. This Thesis was approved for publication on 2017-04-27 at 16:33. DSpace SAF Submission Ingestion Package generated from Vireo submission #11092 on 2017-08-10 at 15:07:11 Made available in DSpace on 2017-08-10T20:33:28Z (GMT). No. of bitstreams: 2 THEKUMPARAMPIL-THESIS-2017.pdf: 795009 bytes, checksum: 601388ce7d30942f5f8da9bcd86da6c0 (MD5) LICENSE.txt: 4217 bytes, checksum: c0a13a9a686f185c3ebdc7c3307af95e (MD5) Previous issue date: 2017-04-27 Embargo set by: Colleen Fallaw for item 102847 Lift date: 2019-08-10T21:27:21Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 102847 on 2019-08-11T09:15:35Z. application/pdf http://hdl.handle.net/2142/97794 en Copyright 2017 Kiran Koshy Thekumparampil Collaborative ranking Recommendation system Revenue management Ordinal (comparison) data Multinomial logit (MNL) model Convex relaxation Nuclear norm minimization Collaborative ranking from ordinal data text text Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Thesis M.S.