Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-53676-7_1 | Lecture Notes in Business Information Processing |
Field | DocType | Volume |
Recommender system,Forgetting,Singular value decomposition,Stochastic gradient descent,Collaborative filtering,Computer science,Matrix decomposition,Theoretical computer science,Throughput,Marketing,Scalability | Conference | 278 |
ISSN | Citations | PageRank |
1865-1348 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alípio Jorge | 1 | 749 | 73.03 |
| João Vinagre | 2 | 57 | 8.56 |
| Marcos Aurélio Domingues | 3 | 97 | 15.33 |
| João Gama | 4 | 3785 | 271.37 |
| Carlos Soares | 5 | 95 | 18.18 |
| Pawel Matuszyk | 6 | 8 | 2.13 |
| Myra Spiliopoulou | 7 | 2297 | 232.72 |