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Permanent link (DOI): https://doi.org/10.7939/R3RV0DD90

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Utilizing Context for Novel Point of Interest Recommendation Open Access

Descriptions

Other title
Subject/Keyword
Collaborative Filtering
Recommender Systems
Location-Based Social Network
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Morawski, Jason M
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)
Miller, James (Electrical and Computer Engineering)
Dick, Scott (Electrical and Computer Engineering)
Liang, Hao (Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization
Software Engineering and Intelligent Systems
Date accepted
2017-05-12T09:08:56Z
Graduation date
2017-11:Fall 2017
Degree
Master of Science
Degree level
Master's
Abstract
Recommender systems are a modern solution for suggesting new items to users. One of their uses is for novel point of interest recommendation, recommending locations to a user which they have not visited. This can be applied to a location-based social network, which contains information about their users' travel history and social connections. Within this context, there are various challenges, such as data sparsity, that limit recommendation effectiveness. We propose an algorithm for personalized novel point of interest recommendation to overcome these challenges. Our solution leverages social, temporal, and spatial context, together with collaborative filtering and a classification algorithm.
Language
English
DOI
doi:10.7939/R3RV0DD90
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Jason Morawski, Torin Stepan, Scott Dick and James Miller, 2017. Novel Point of Interest Recommendation with Location-Based Social Networks. Manuscript submitted for publication.

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