Collaborative ranking from ordinal data

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Thekumparampil, Kiran Koshy

Contributors dc:contributor

• Oh, Sewoong

• Collaborative ranking
• Recommendation system
• Revenue management
• Ordinal (comparison) data
• Multinomial logit (MNL) model
• Convex relaxation
• Nuclear norm minimization

Statement dc:rights

• Copyright 2017 Kiran Koshy Thekumparampil

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/97794

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/97794