Please use this identifier to cite or link to this item:
Title: Scalable Online Top-N Recommender Systems
Authors: Alípio Jorge
João Marques Silva
João Gama
Carlos Manuel Soares
Issue Date: 2017
Abstract: Given the large volumes and dynamics of data that recommender systems currently have to deal with, we look at online stream based approaches that are able to cope with high throughput observations. In this paper we describe work on incremental neighborhood based and incremental matrix factorization approaches for binary ratings, starting with a general introduction, looking at various approaches and describing existing enhancements. We refer to recent work on forgetting techniques and multidimensional recommendation. We will also focus on adequate procedures for the evaluation of online recommender algorithms. © Springer International Publishing AG 2017.
metadata.dc.type: conferenceObject
Appears in Collections:CESE - Articles in International Conferences
LIAAD - Articles in International Conferences

Files in This Item:
File Description SizeFormat 
P-00M-EY5.pdf316.77 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.