Authors:
Najla Maalaoui
;
Raoudha Beltaifa
and
Lamia Jilani
Affiliation:
RIADI Lab., National School of Computer Sciences, Manouba University, Tunisia
Keyword(s):
Service Oriented Dynamic Software Product Lines, Recommender System, User Requirements, Ontology, Deep Neural Network.
Abstract:
Today’s demand for customized service-based systems requires that industry understands the context and the particular needs of their customers. Service Oriented Dynamic Software Product Line practices enable companies to create individual products for every customer by providing an interdependent set of features presenting web services that are automatically activated and deactivated depending on the running situation. Such product lines are designed to support their self-adaptation to new contexts and requirements. Users configure personalized products by selecting desired features based on their needs. However, with large feature models, users must understand the functionalities of features and the impact of their gradual selections and their current context in order to make appropriate decisions. Thus, users need to be guided in configuring their product. To tackle this challenge, users can express their product requirements by textual language and a recommended product will be ge
nerated with respect to the described requirements. In this paper, we propose a deep neural network based recommendation approach that provides personalized recommendations to users which ease the configuration process. In detail, our proposed recommender system is based on a deep neural network that predicts to the user relevant features of the recommended product with the consideration of their requirements, contextual data and previous recommended products. In order to demonstrate the performance of our approach, we compared six different recommendation algorithms in a smart home case study.
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