@article {854,
	title = {Using Prediction Confidence Factors to Enhance Collaborative Filtering Recommendation Quality},
	journal = {Technologies},
	volume = {13},
	year = {2025},
	pages = {181},
	abstract = {<p>Recommender systems suggest items that users are likely to accept by predicting ratings for items they have not already rated. Collaborative filtering is a widely used method that produces these predictions, based on the ratings of similar users, termed as near neighbors. However, in many cases, prediction errors occur and, therefore, the recommender system ends up either recommending unwanted products or missing out on products the user would actually desire. As a result, the quality of the recommendations that are produced is of major importance. In this paper, we introduce an advanced collaborative filtering recommendation algorithm that upgrades the quality of the recommendations that are produced by considering, along with the rating prediction value of the items computed by the plain collaborative filtering procedure, a number of confidence factors that each rating prediction fulfills. The presented algorithm maintains high recommendation coverage, and can be applied to every collaborative filtering dataset, since it is based only on the very basic information. Based on the application of the algorithm on widely used recommender systems datasets, the proposed algorithm significantly upgrades the recommendation quality, surpassing the performance of state-of-the-art research works that also consider confidence factors.</p>
},
	keywords = {Algorithm, collaborative filtering, prediction confidence factors, recommendation quality, Recommender Systems},
	issn = {2227-7080},
	doi = {https://doi.org/10.3390/technologies13050181},
	url = {https://doi.org/10.3390/technologies13050181},
	author = {Dionisis Margaris and Spiliotopoulos, Dimitris and Sgardelis, Kiriakos and Costas Vassilakis}
}
@conference {802,
	title = {A User Interface for Personalising WS-BPEL Scenarios},
	booktitle = {Proceedings of the 2021 International Conference on Human-Computer Interaction: HCI in Business, Government and Organizations},
	year = {2021},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {<p>Due to the huge volume of web services available, both locally and in the cloud, the performance of users and systems need significant research attention. Since WS-BPEL is the dominant language for orchestrating individual web services into business processes, by composing WS-BPEL scripts/scenarios, graphical notations facilitating WS-BPEL design can be extremely useful. Current user interfaces allow WS-BPEL designers not only to invoke selected web services, but also to explicitly ask for recommendations. Then, the user interface appends the services achieving the highest score, according to the attributes{\textquoteright} importance, set by the designer, to their WS-BPEL scenario. However, since the final selection is produced automatically, rather than relying on the designers{\textquoteright} choices, many times, from a personalisation point of view, the adaptation fails. This work reports on the design, development and user evaluation of a user interface that incorporates functionalities that support the designers{\textquoteright} selection performance, thereby upgrading the personalisation level of the WS-BPEL scenarios, as well as the success of the adaptation.</p>
},
	keywords = {Personalisation, Recommender Systems, Usability, User evaluation, user interface, Web services, WS-BPEL scenarios},
	isbn = {978-3-030-77750-0},
	doi = {https://doi.org/10.1007/978-3-030-77750-0_25},
	author = {Dionisis Margaris and Spiliotopoulos, Dimitris and Vasilopoulos, Dionysios and Costas Vassilakis}
}
