This year WEARS will be co-located with The International Conference on Evaluation and Assessment in Software Engineering (EASE).
Recommender systems in software engineering (RSSE) have gained momentum in recent years. Such systems assist developers in navigating large information spaces and getting instant recommendations that might be helpful to solve their development tasks. Though a lot of improvements have been obtained so far, there is still the need to make RSSE more effective and efficient. Furthermore, while several attempts have been made to improve the recommendation accuracy, little attention has been paid to make such systems robust and resilient to malicious data. In fact, by manipulating training data available in open source software platforms, a hostile user may render recommender systems vulnerable to adversarial attacks, putting software clients at risk. The First International Workshop on Evaluation and Analysis of Recommender Systems in Software Engineering (WEARS 2021) brings in a forum for researchers and practitioners to share, discuss and explore the opportunities and challenges raised by the evaluation and in-depth investigation of RSSE. We solicit research work from the Software Engineering community as well as related fields such as Machine Learning and Recommender Systems.
The topics of interest include, but are by no means limited to:
Submission deadline: April 2, 2021. (23:59 PM AOE)
Notification of acceptance: April 19, 2021.
Camera-ready copy due: TBA
Workshop: June 23, 2021.
Tommaso Di Noia is Full Professor of Information and Data Management with the Polytechnic University of Bari, Italy. Currently, his research activity focuses mainly on the themes that revolve around Artificial Intelligence and Data Management with reference to techniques and applications of machine learning and recommender systems. Recently, his attention has shifted to how to exploit the information encoded within Big Data datasets, such as those available thanks to the Linking Open Data initiative, in order to create content-based recommendation engines (content-based) or hybrid ones. On the issue of personalized information access and the modeling of user preferences, innovative solutions have recently been proposed that combine and integrate different aspects, solutions and techniques from Artificial Intelligence. Of great interest to his research is the security of Artificial Intelligence systems with particular reference to Adversarial Machine Learning.
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