Paper Info
Title
A Context-Aware Recommender System for Extended Software Product Line Configurations.
Abstract
Mass customization of standardized products has become a trend to succeed in todayu0027s market environment. Software Product Lines (SPLs) address this trend by describing a family of software products that share a common set of features. However, choosing the appropriate set of features that matches a useru0027s individual interests is hampered due to the overwhelming amount of possible SPL configurations. Recommender systems can address this challenge by filtering the number of configurations and suggesting a suitable set of features for the useru0027s requirements. In this paper, we propose a context-aware recommender system for predicting feature selections in an extended SPL configuration scenario, i.e. taking nonfunctional properties of features into consideration. We present an empirical evaluation based on a large real-world dataset of configurations derived from industrial experience in the Enterprise Resource Planning domain. Our results indicate significant improvements in the predictive accuracy of our context-aware recommendation approach over a state-of-the-art binary-based approach.
Year
DOI
Venue
2018
10.1145/3168365.3168373
VaMoS
Keywords
Field
DocType
Software Product Lines,Feature Model,Non-Functional Properties,Configuration,Recommender Systems
Mass customization,Recommender system,Data mining,Enterprise resource planning,Computer science,Market environment,Filter (signal processing),Software,Feature model,Software product line
Conference
Citations 
PageRank 
References 
2
0.35
23
Authors
5
Name
Order
Citations
PageRank
Juliana Alves Pereira1908.44
Sandro Schulze225923.43
Sebastian Krieter38513.81
Márcio Ribeiro436332.81
Gunter Saake53255639.75