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.
%0 Book Section
%1 Jorge2017
%A Jorge, Alípio M.
%A Vinagre, João
%A Domingues, Marcos
%A Gama, João
%A Soares, Carlos
%A Matuszyk, Pawel
%A Spiliopoulou, Myra
%B E-Commerce and Web Technologies: 17th International Conference, EC-Web 2016, Porto, Portugal, September 5-8, 2016, Revised Selected Papers
%D 2017
%E Bridge, Derek
%E Stuckenschmidt, Heiner
%I Springer International Publishing
%K myown
%P 3--20
%R 10.1007/978-3-319-53676-7_1
%T Scalable Online Top-N Recommender Systems
%U https://doi.org/10.1007/978-3-319-53676-7_1
%X 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.
%@ 978-3-319-53676-7
@inbook{Jorge2017,
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.},
added-at = {2017-10-13T14:33:34.000+0200},
author = {Jorge, Alípio M. and Vinagre, João and Domingues, Marcos and Gama, João and Soares, Carlos and Matuszyk, Pawel and Spiliopoulou, Myra},
biburl = {https://www.bibsonomy.org/bibtex/2ee7c5f1c588205c92360c64be5bd037d/matuszyk},
booktitle = {E-Commerce and Web Technologies: 17th International Conference, EC-Web 2016, Porto, Portugal, September 5-8, 2016, Revised Selected Papers },
doi = {10.1007/978-3-319-53676-7_1},
editor = {Bridge, Derek and Stuckenschmidt, Heiner},
interhash = {4cb9e96a3176f64f0d001c4cee9733d9},
intrahash = {ee7c5f1c588205c92360c64be5bd037d},
isbn = {978-3-319-53676-7},
keywords = {myown},
pages = {3--20},
publisher = {Springer International Publishing},
timestamp = {2017-10-13T14:33:34.000+0200},
title = {Scalable Online Top-N Recommender Systems},
url = {https://doi.org/10.1007/978-3-319-53676-7_1},
year = 2017
}