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Permanent link (DOI): https://doi.org/10.7939/R3ZM2H

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Incorporating Content and Context in Recommender Systems Open Access

Descriptions

Other title
Subject/Keyword
cold-start
hybrid
k-nearest neighbors
spatial
location based social networks
books
collaborative filtering
temporal
fuzzy
context
fuzzy taste vector
similarity
content
social
rural libraries
movies
recommendation
recommender
university digital libraries
classifier
locations
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Stepan, Torin KS
Supervisor and department
Miller, James (Electrical and Computer Engineering)
Dick, Scott (Electrical and Computer Engineering)
Examining committee member and department
Niu, Di (Electrical and Computer Engineering)
Reformat, Marek (Electrical and Computer Engineering)
Dick, Scott (Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Software Engineering and Intelligent Systems
Date accepted
2015-01-23T14:10:05Z
Graduation date
2015-06
Degree
Master of Science
Degree level
Master's
Abstract
Recommender systems are a growing area of research that find practical applications in a variety of domains. Integrated library systems and location-based social networks can apply recommendation algorithms to assist their users in finding an item or location that suits their needs. With an ever-increasing variety of options to choose from, deciding on which book to read or movie to watch can become overwhelming. Recommender systems aid their users in the decision making process by providing a list of items likely to be relevant to the user's needs and interests. A persistent issue faced by recommender systems is a lack of data concerning the preferences of its users, known as the "cold-start" problem, which leads to poor recommendation quality, particularly for new users and items. To improve recommendation quality in the face of incomplete data, we propose several novel approaches for incorporating all available data into collaborative filtering algorithms.
Language
English
DOI
doi:10.7939/R3ZM2H
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Stepan, T., Dick, S. & Miller, J. (2014). A Fuzzy Recommender System for Public Library Catalogs. IEEE Transactions on Fuzzy Systems, submitted.Stepan, T., Dick, S. & Miller, J. (2014). Incorporating Spatial, Temporal, and Social Context in Recommendations for Location-Based Social Networks. ACM Transactions on Information Systems, submitted.

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