Name
Abstract | ||
|---|---|---|
•We adapt six state-of-the-art recommendation algorithms to the context of product-line configuration.•We empirically evaluate how well our proposed algorithms are capable of understanding the preferences of the users.•We compare the configuration quality from these recommendation algorithms and a random recommender.•We demonstrate their usability on the two largest real-world datasets of configurations already cited in the literature. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.cl.2018.01.003 | Computer Languages, Systems & Structures |
Keywords | Field | DocType |
Product lines,Feature model,Product-line configuration,Recommender systems,Personalized recommendations | Recommender system,Configurator,Functional requirement,Weighting,Software engineering,Reuse,Computer science,Usability,Theoretical computer science,Product line | Journal |
Volume | ISSN | Citations |
54 | 1477-8424 | 2 |
PageRank | References | Authors |
0.36 | 35 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juliana Alves Pereira | 1 | 90 | 8.44 |
| Pawel Matuszyk | 2 | 8 | 2.13 |
| Sebastian Krieter | 3 | 85 | 13.81 |
| Myra Spiliopoulou | 4 | 2297 | 232.72 |
| Gunter Saake | 5 | 3255 | 639.75 |