@article {833,
	title = {Experimental results for Evaluating User Similarity Metrics in Sparse Collaborative Filtering Datasets},
	number = {TR-24001},
	year = {2024},
	month = {03/2024},
	institution = {SODA lab},
	type = {Technical Report},
	address = {Tripolis, Greece},
	keywords = {collaborative filtering, Evaluation, Sparse Datasets, User similarity metrics},
	issn = {SODA-TR-24001},
	author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis}
}
@conference {830,
	title = {A User Interface for Tuning QoS parameters in Recommendation-Based Business Process Scenario Adaptation},
	booktitle = {Proceedings of the 19th International Conference on Web Information Systems and Technologies (WEBIST 2023)},
	year = {2023},
	abstract = {<p>The Web Services Business Process Execution Language (BPEL) is a special-purpose language that orchestrates web services into a high-level business process. A typical BPEL scenario contains invocations to preselected web services, along with their parameters. However, many recent research works support dynamic service selection, based on user-set policies and criteria. Furthermore, users may request a service recommendation, in which case functionally equivalent service offerings by different providers will be considered by the personalization module. Along with the recommendation request, users provide the policy parameters, which include minimum and maximum bounds for the non-functional attributes concerning the service, and the system exploits these bounds to select and use the optimal candidate services. However, in many real-life cases, a person will accept/purchase a product or a service that exceeds the threshold(s) that initially he/she has set, e.g. if the overhead is marginal or the offer is deemed appealing, or no satisfactory service candidates are identified using the initial settings. In this paper, we present and evaluate a specialized User Interface that allows the user to review service candidates marginally exceeding the specified bounds, and consider them while making the final service selection.</p>
},
	keywords = {adaptation, business processes, Personalisation, Recommendation, Recommender Systems, UI, Web services, WS-BPEL},
	author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis}
}
