Personalized recommender systems for product-line configuration processes

Abstract Product lines are designed to support the reuse of features across multiple products. Features are product functional requirements that are important to stakeholders. In this context, feature models are used to establish a reuse platform and allow the configuration of multiple products through the interactive selection of a valid combination of features. Although there are many specialized configurator tools that aim to provide configuration support, they only assure that all dependencies from selected features are automatically satisfied. However, no support is provided to help decision makers focus on likely relevant configuration options. Consequently, since decision makers are often unsure about their needs, the configuration of large feature models becomes challenging. To improve the efficiency and quality of the product configuration process, we propose a new approach that provides users with a limited set of permitted, necessary and relevant choices. To this end, we adapt six state-of-the-art recommender algorithms to the product line configuration context. We empirically demonstrate the usability of the implemented algorithms in different domain scenarios, based on two real-world datasets of configurations. The results of our evaluation show that recommender algorithms, such as CF-shrinkage, CF-significance weighting, and BRISMF, when applied in the context of product-line configuration can efficiently support decision makers in a most efficient selection of features.