In recent years, recommender systems have gained an increasingly crucial role in software engineering. Such systems allow developers to exploit a plethora of reusable artifacts, including source code and documentation, which can support the development activities. However, recommender systems are complex tools that are difficult to personalize or fine-tune if developers want to improve them for increasing the relevance of the retrievable recommendations. In this paper, we propose a low-code development approach to engineering recommender systems. Low-code platforms enable the creation and deployment of fully functional applications by mainly using visual abstractions and interfaces and requiring little or no procedural code. Thus, we aim to foster a low-code way of building recommender systems by means of a metamodel to represent the peculiar components. Then, dedicated supporting tools are also proposed to help developers easily model and build their custom recommender systems. Preliminary evaluations of the approach have been conducted by reimplementing real recommender systems, confirming the feasibility of developing them in a low-code manner.

Democratizing the development of recommender systems by means of low-code platforms

Di Sipio C.;Di Ruscio D.;NGUYEN THANH PHUONG
2020

Abstract

In recent years, recommender systems have gained an increasingly crucial role in software engineering. Such systems allow developers to exploit a plethora of reusable artifacts, including source code and documentation, which can support the development activities. However, recommender systems are complex tools that are difficult to personalize or fine-tune if developers want to improve them for increasing the relevance of the retrievable recommendations. In this paper, we propose a low-code development approach to engineering recommender systems. Low-code platforms enable the creation and deployment of fully functional applications by mainly using visual abstractions and interfaces and requiring little or no procedural code. Thus, we aim to foster a low-code way of building recommender systems by means of a metamodel to represent the peculiar components. Then, dedicated supporting tools are also proposed to help developers easily model and build their custom recommender systems. Preliminary evaluations of the approach have been conducted by reimplementing real recommender systems, confirming the feasibility of developing them in a low-code manner.
9781450381352
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/153742
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