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<p>While the benefits of product recommender systems (RS) are prominent, due to the complexity of recommendation algorithms and data models, it is difficult for businesses to deploy such systems on their e-stores. Related works that tackle RS development complexity do not entirely abstract the technical details from developers; thus, margins for improvement exist. Moreover, these works do not offer solutions that eliminate the need to write code by developers. These are the motivations for the Ubiquitous Context-Aware Recommender Systems (UbiCARS) Framework proposed in the current work. UbiCARS utilize user feedback acquisition techniques from both e-stores and physical stores to offer recommendations. The framework aims to reduce development complexity, abstract technical details and expedite the development of UbiCARS (facilitating both e-stores and physical stores) by non-RS experts. This is achieved through a Model-Driven Development methodology, that uses a model-based configuration process where models of recommender systems drive the dynamic configuration of UbiCARS on e-stores. The framework was evaluated with developers and experts via the survey method. The evaluation results show the framework’s potential.</p>

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An MDD Framework Towards the Automated Development of Ubiquitous Context-Aware Recommender Systems for Commerce

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月10日

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  • Christos Mettouris,
  • Achilleas Achilleos,
  • Georgia Kapitsaki,
  • George A. Papadopoulos

摘要

While the benefits of product recommender systems (RS) are prominent, due to the complexity of recommendation algorithms and data models, it is difficult for businesses to deploy such systems on their e-stores. Related works that tackle RS development complexity do not entirely abstract the technical details from developers; thus, margins for improvement exist. Moreover, these works do not offer solutions that eliminate the need to write code by developers. These are the motivations for the Ubiquitous Context-Aware Recommender Systems (UbiCARS) Framework proposed in the current work. UbiCARS utilize user feedback acquisition techniques from both e-stores and physical stores to offer recommendations. The framework aims to reduce development complexity, abstract technical details and expedite the development of UbiCARS (facilitating both e-stores and physical stores) by non-RS experts. This is achieved through a Model-Driven Development methodology, that uses a model-based configuration process where models of recommender systems drive the dynamic configuration of UbiCARS on e-stores. The framework was evaluated with developers and experts via the survey method. The evaluation results show the framework’s potential.