Publications
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Author Title [ Type
] Year Filters: Keyword is collaborative filtering and Author is Dimitris Spiliotopoulos [Clear All Filters]
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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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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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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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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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2022. Persona Finetuning for Online Gaming using Personalisation Techniques. Proceedings of the 2022 HCII Conference.
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2019. Social Relations versus Near Neighbours: Reliable Recommenders in Limited Information Social Network Collaborative Filtering for Online Advertising. Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019).
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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. Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering. Applied Sciences. 11:8369.
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2022. On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets. Information. 13:302.
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2023. Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets. Big Data and Cognitive Computing. 7:59.
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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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2024. Experimental results for Evaluating User Similarity Metrics in Sparse Collaborative Filtering Datasets. SODA Lab Technical Reports.