@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}
}
@conference {785,
	title = {A User Interface for Personalized Web Service Selection in Business Processes},
	booktitle = {Lecture Notes in Computer Science},
	year = {2020},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {<p>Nowadays, due to the huge volume of information available on the web, the need for personalization is more than necessary. Choosing the right information for each user is as important as the way this information is presented to him or her. Currently, user-triggered recommendation requests for web services are implemented as an automatic recommendation based on parametric computation. This work reports on a specialized user interface for business processes, where writing code entails invocation of business process information. The paper presents the user interface design for Personalized Web Service Selection in Business Process scenario execution and the user evaluation by business process engineers.</p>
},
	keywords = {adaptation, business processes, personalization, user interface, Web services, WS-BPEL},
	doi = {10.1007/978-3-030-60152-2_41},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis and Gregory Karagiorgos}
}
@article {margaris-tldks,
	title = {Enhancing User Rating Database Consistency Through Pruning},
	journal = {Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIV},
	volume = {LNCS, volume 10620},
	number = {Special issue on Consistency and Inconsistency in Data-centric Applications},
	year = {2017},
	pages = {33{\textendash}64},
	publisher = {Springer Berlin Heidelberg},
	abstract = {Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by the interests and likings of people may change: people listen to different singers or even different types of music, watch different types of movies, read different types of books and so on. Due to this type of changes, an amount of inconsistency is introduced in the database since a portion of it does not reflect the current preferences of the user, which is its intended purpose. In this paper, we present a pruning technique that removes old aged user behavior data from the ratings database, which are bound to correspond to invalidated preferences of the user. Through pruning (1) inconsistencies are removed and data quality is upgraded, (2) better rating prediction generation times are achieved and (3) the ratings database size is reduced. We also propose an algorithm for determining the amount of pruning that should be performed, allowing the tuning and operation of the pruning algorithm in an unsupervised fashion. The proposed technique is evaluated and compared against seven aging algorithms, which reduce the importance of aged ratings, and a state-of-the-art pruning algorithm, using datasets with varying characteristics. It is also validated using two distinct rating prediction computation strategies, namely collaborative filtering and matrix factorization. The proposed technique needs no extra information concerning the items{\textquoteright} characteristics (e.g. categories that they belong to or attributes{\textquoteright} values), can be used in all rating databases that include a timestamp and has been proved to be effective in any size of users-items database and under two rating prediction computation strategies. Note: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author{\textquoteright}s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.},
	keywords = {Collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services},
	doi = {10.1007/978-3-662-55947-5_3},
	url = {https://link.springer.com/article/10.1007\%2Fs11761-017-0216-y},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@article {margaris-soca-17,
	title = {Exploiting Internet of Things information to enhance venues{\textquoteright} recommendation accuracy},
	journal = {Service Oriented Computing and Applications},
	volume = {todb},
	year = {2017},
	month = {aug},
	pages = {393{\textendash}409},
	publisher = {Springer Nature},
	abstract = {In this paper, we introduce a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations. The proposed algorithm provides added value for the web services offered by the Internet of Things and enhances the state-of-the-art in this algorithm category by taking into account (a) web of things data regarding the contexts of the user and the context of the venues to be recommended (restaurants, movie theatres, etc.), such as the user{\textquoteright}s geographical position, road traffic and weather conditions, (b) qualitative aspects of the venues, such as price, atmosphere or service, (c) the semantic similarity of venues and (d) the influencing factors between social network users, derived from user participation in social networks. The combination of these features leads to more accurate and better user-targeted recommendations. We also present a framework which incorporates the above characteristics, and we evaluate the presented algorithm, both in terms of performance and recommendation quality.},
	keywords = {collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services},
	doi = {10.1007/s11761-017-0216-y},
	url = {https://link.springer.com/article/10.1007/s11761-017-0216-y},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@conference {marg15a,
	title = {A Collaborative Filtering Algorithm with Clustering for Personalized Web Service Selection in Business Processes},
	booktitle = {Proceedings of RCIS 2015},
	year = {2015},
	month = {5},
	abstract = {Recommender systems aim to propose items that are expected to be of interest to the users. As one of the most successful approaches to building recommender systems, collaborative filtering exploits the known preferences of a group of users to formulate recommendations or predictions of the unknown preferences for other users. In many cases, collaborative filtering algorithms handle complex items, which are described using hierarchical tree structures containing rich semantic information. In order to make accurate recommendations on such items, the related algorithms must examine all aspects of the available semantic information. Thus, when collaborative filtering techniques are employed to adapt the execution of business processes, they must take into account the services{\textquoteright} Quality of Service parameters, so as to generate recommendations tailored to the individual user needs. In this paper, we present a collaborative filtering-based algorithm which takes into account the web services{\textquoteright} QoS parameters in order to tailor the execution of business processes to the preferences of users. An offline clustering technique is also introduced for supporting the efficient and scalable execution of proposed algorithm under the presence of large repositories of sparse data.},
	keywords = {business processes, clustering, collaborative filtering, hierarchical tree, performance, Quality of service, Web services},
	doi = {10.1109/RCIS.2015.7128877},
	author = {Dionisis Margaris and P. Georgiadis and Costas Vassilakis}
}
