Publications
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2015. An integrated framework for adapting WS-BPEL scenario execution using QoS and collaborative filtering techniques. Science of Computer Programming. 98:707–734.
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2020. Improving collaborative filtering’s rating prediction accuracy by considering users’ dynamic rating variability. International Journal of Big Data Intelligence. 7(2)
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2016. Pruning and Aging for User Histories in Collaborative Filtering. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence.
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2025. From Rating Predictions to Reliable Recommendations in Collaborative Filtering: The Concept of Recommendation Reliability Classes. Big Data and Cognitive Computing. 9:106.
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2020. A User Interface for Personalized Web Service Selection in Business Processes. Lecture Notes in Computer Science.
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2018. Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems. Informatics. 5:21pages.
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2013. Adapting WS-BPEL scenario execution using collaborative filtering techniques. IEEE 7th International Conference on Research Challenges in Information Science (RCIS).
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2016. Improving QoS Delivered by WS-BPEL Scenario Adaptation through Service Execution Parallelization. Proceedings of the 31st Annual ACM Symposium on Applied Computing. :1590-1596.
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2021. Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering. Applied Sciences. 11:8369.
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2020. Improving collaborative filtering’s rating prediction coverage in sparse datasets by exploiting the “friend of a friend” concept. International Journal of Big Data Intelligence. 7(1)
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2017. Exploiting Internet of Things information to enhance venues' recommendation accuracy. Service Oriented Computing and Applications. todb:393–409.
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2020. What makes a review a reliable rating in recommender systems? Information Processing & Management. 57(6):102304.
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2017. Query personalization using social network information and collaborative filtering techniques. Future Generation of Computer Systems. Special Issue on Recommender Systems for Large-Scale Social Networks:440–450.
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2019. Improving Collaborative Filtering’s Rating Prediction Quality by Exploiting the Item Adoption Eagerness Information. Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence.
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2017. Using Time Clusters for Following Users’ Shifts in Rating Practices. Complex Systems Informatics and Modeling Quarterly. 13
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2022. Anchoring Effect Mitigation for Complex Recommender System Design. Proceedings of the 2022 HCI International Conference.
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2015. A Collaborative Filtering Algorithm with Clustering for Personalized Web Service Selection in Business Processes. Proceedings of RCIS 2015.
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2019. Improving Collaborative Filtering’s Rating Prediction Coverage in Sparse Datasets through the Introduction of Virtual Near Neighbors. Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019).
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2017. Enhancing User Rating Database Consistency Through Pruning. Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIV. LNCS, volume 10620:33–64.
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2020. An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings. Algorithms. 13:174.
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2021. A User Interface for Personalising WS-BPEL Scenarios. Proceedings of the 2021 International Conference on Human-Computer Interaction: HCI in Business, Government and Organizations.
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2024. Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering. Big Data and Cognitive Computing. 8:53.
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2019. Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks. Social Network Analysis and Mining. 9:64.
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2018. Improving Collaborative Filtering's Rating Prediction Coverage in Sparse Datasets by Exploiting User Dissimilarity. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).