Publication
Title
Unifying nearest neighbors collaborative filtering
Author
Abstract
We study collaborative filtering for applications in which there exists for every user a set of items about which the user has given binary, positive-only feedback (one-class collaborative filtering). Take for example an on-line store that knows all past purchases of every customer. An important class of algorithms for one-class collaborative filtering are the nearest neighbors algorithms, typically divided into user-based and item-based algorithms. We introduce a reformulation that unifies user- and item-based nearest neighbors algorithms and use this reformulation to propose a novel algorithm that incorporates the best of both worlds and outperforms state-of-the-art algorithms. Additionally, we propose a method for naturally explaining the recommendations made by our algorithm and show that this method is also applicable to existing user-based nearest neighbors methods.
Language
English
Source (book)
Proceedings of the 8th ACM Conference on Recommender Systems
Publication
New York, N.Y. : ACM, 2014
ISBN
978-1-4503-2668-1
Volume/pages
p. 177-184
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identification
Creation 07.04.2015
Last edited 22.11.2016
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