Scalable Online Top-N Recommender Systems

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Jorge, AM (Author) FCUP; Vinagre, J (Author) Other; Domingues, M (Author) Other; João Gama (Author) FEP; Carlos Soares (Author) FEUP; Matuszyk, P (Author) Other; Spiliopoulou, M (Author) Other

Conference proceedings International

Title: E-COMMERCE AND WEB TECHNOLOGIES, EC-WEB 2016

Pages: 3-20

17th International Conference on E-Commerce and Web Technologies (EC-Web)

Porto, PORTUGAL, SEP 05-08, 2016

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-00M-EY5

DOI: 10.1007/978-3-319-53676-7_1

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 18