Publications
] .
2020. Improving collaborative filtering’s rating prediction accuracy by considering users’ dynamic rating variability. International Journal of Big Data Intelligence. 7(2)
.
2013. Adapting WS-BPEL scenario execution using collaborative filtering techniques. IEEE 7th International Conference on Research Challenges in Information Science (RCIS).
.
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.
.
2018. Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems. Informatics. 5:21pages.
.
2020. A User Interface for Personalized Web Service Selection in Business Processes. Lecture Notes in Computer Science.
.
2017. Exploiting Internet of Things information to enhance venues' recommendation accuracy. Service Oriented Computing and Applications. todb:393–409.
.
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.
.
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)
.
2015. A Collaborative Filtering Algorithm with Clustering for Personalized Web Service Selection in Business Processes. Proceedings of RCIS 2015.
.
2021. Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering. Applied Sciences. 11:8369.
.
2017. Enhancing User Rating Database Consistency Through Pruning. Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIV. LNCS, volume 10620:33–64.
.
2017. Using Time Clusters for Following Users’ Shifts in Rating Practices. Complex Systems Informatics and Modeling Quarterly. 13
.
2020. What makes a review a reliable rating in recommender systems? Information Processing & Management. 57(6):102304.
.
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.
.
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).
.
2017. Improving Collaborative Filtering's Rating Prediction Quality in Dense Datasets, by Pruning Old Ratings. Proceedings of the 22nd IEEE Symposium on Computers and Communications (ISCC17).
.
2022. Anchoring Effect Mitigation for Complex Recommender System Design. Proceedings of the 2022 HCI International Conference.
.
2013. An integrated framework for QoS-based adaptation and exception resolution in WS-BPEL scenarios. Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC \textquotesingle13.
.
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).
.
2020. An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings. Algorithms. 13:174.
.
2020. Human behaviour in multimodal interaction: main effects of civic action and interpersonal and problem-solving skills. Journal of Ambient Intelligence and Humanized Computing. TBD:TBD.
.
2004. A web browser for ISDN card phones. Journal of Internet Technology. 5:267-277.
.
2001. Agents in Decentralised Information Ecosystems: The DIET Approach. Symposium on Information Agents for E-Commerce, AISB Convention.
.
2001. Agents in Decentralised Information Ecosystems: The DIET Approach. Proccedings of the Symposium on Information Agents for E-Commerce, AISB Convention. :109–117.