Publications
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] Title Type Year Filters: First Letter Of Last Name is M [Clear All Filters]
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2006. Virtual reality in the e-Society. Virtual Reality. 11:71-73.
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2003. CPM: A Cube Presentation Model for OLAP. Data Warehousing and Knowledge Discovery, 5th International Conference, DaWaK 2003, Prague, Czech Republic, September 3-5,2003, Proceedings. :4–13.
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2005. A Presentation Model & Non-Traditional Visualization for OLAP. IJDWM. 1:1–36.
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2003. Advanced visualization for OLAP. DOLAP 2003, ACM Sixth International Workshop on Data Warehousing and OLAP, New Orleans, Louisiana, USA, November 7, 2003, Proceedings. :9–16.
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2011. ERA: Efficient Serial and Parallel Suffix Tree Construction for Very Long Strings. CoRR. abs/1109.6884
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2011. ERA: Efficient Serial and Parallel Suffix Tree Construction for Very Long Strings. PVLDB. 5:49–60.
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2017. Knowledge-Based Leisure Time Recommendations in Social Networks. Current Trends on Knowledge-Based Systems. :23–48.
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2019. Improving Collaborative Filtering’s Rating Prediction Accuracy by Introducing the Common Item Rating Past Criterion. Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019).
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2015. On Replacement Service Selection in WS-BPEL Scenario Adaptation. Proceedings of the 8th IEEE International Conference on Service Oriented Computing & Applications (SOCA 2015).
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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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2018. Improving Collaborative Filtering's Rating Prediction Accuracy by Considering Users' Rating Variability. 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).
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2022. Anchoring Effect Mitigation for Complex Recommender System Design. Proceedings of the 2022 HCI International Conference.
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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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2016. Pruning and Aging for User Histories in Collaborative Filtering. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence.
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2021. On Addressing the Low Rating Prediction Coverage in Sparse Datasets Using Virtual Ratings. SN Computer Science. 2:255.
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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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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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2020. An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings. Algorithms. 13:174.
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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. Identifying Reliable Recommenders in Users' Collaborating Filtering and Social Neighbourhoods. Lecture Notes in Social Networks. :51–76.
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2018. Enhancing Rating Prediction Quality Through Improving the Accuracy of Detection of Shifts in Rating Practices. Lecture Notes in Computer Science. 10940 (LNCS):151–191.
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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. Exploiting Internet of Things information to enhance venues' recommendation accuracy. Service Oriented Computing and Applications. todb:393–409.
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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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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)