Personalized recommender systems for product-line configuration processes. (December 2018)

• Record Type: Journal Article
• Authors: Pereira, Juliana Alves; Matuszyk, Pawel; Krieter, Sebastian; Spiliopoulou, Myra; Saake, Gunter
• Is Part Of: Computer languages, systems & structures. Volume 54(2018)
• Page Start: 451
• Page End: 471
• Publication Date: 2018-12
• Subjects: Product lines -- Feature model -- Product-line configuration -- Recommender systems -- Personalized recommendations; Programming languages (Electronic computers) -- Periodicals; Computer networks -- Periodicals; Computer architecture -- Periodicals; Computer systems -- Periodicals; Langage de programmation; Réseau d'ordinateurs; Architecture d'ordinateur; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 005.13
• DOI: 10.1016/j.cl.2018.01.003
• Languages: English
• ISSNs: 1477-8424

Abstract

Highlights: 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. 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.