@conference {839,
	title = {Improving Recommendation Quality in Collaborative Filtering by Including Prediction Confidence Factors},
	booktitle = {Proceedings of the 20th International Conference on Web Information Systems and Technologies (WEBIST 24)},
	year = {2024},
	publisher = {SCITEPRESS Digital Library},
	organization = {SCITEPRESS Digital Library},
	abstract = {<p>Collaborative filtering is a prevalent recommender system technique which generates rating predictions based on the rating values given by the users{\textquoteright} near neighbours. Consequently, for each user, the items scoring the highest prediction values are recommended to them. Unfortunately, predictions inherently entail errors, which, in the case of recommender systems, manifest as unsuccessful recommendations. However, along with each rating prediction value, prediction confidence factors can be computed. As a result, items having low prediction confidence factor values, can be either declined for recommendation or have their recommendation priority demoted. In the former case, some users may receive fewer recommended items or even none, especially when using a sparse dataset. In this paper, we present an algorithm that determines the items to be recommended by considering both the rating prediction values and confidence factors of predictions, allowing for predictions with higher confidence factors to outrank predictions with higher value, but lower confidence. The presented algorithm achieves to enhance the recommendation quality, while at the same time retaining the number of recommendations for each user.</p>
},
	keywords = {Algorithm, Certainty factors, collaborative filtering, Rating Predictions, Recommender Systems},
	author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis and Stefanos Ougiaroglou}
}
@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}
}
