Publications
Export 32 results:
Author Title Type [ Year
] Filters: Keyword is collaborative filtering [Clear All Filters]
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2026. Optimizing Collaborative Filtering for Accurate Rating Predictions in Very Sparse Datasets. Future Internet. 18:114.
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2026. Optimizing Collaborative Filtering in Very Sparse Datasets: a Confidence-based Approach. Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications.
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2026. Unveiling Structural Distributional Bias in Collaborative Filtering for Ultra-Sparse Datasets. Proceedings of the 2026 International Conference on Information, Intelligence, Systems and Applications.
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2025. An evaluation review of user similarity metrics in sparse collaborative filtering datasets. International Journal of Data Science and Analytics.
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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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2025. Using Prediction Confidence Factors to Enhance Collaborative Filtering Recommendation Quality. Technologies. 13:181.
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2024. Experimental results for Evaluating User Similarity Metrics in Sparse Collaborative Filtering Datasets. SODA Lab Technical Reports.
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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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2024. Improving Recommendation Quality in Collaborative Filtering by Including Prediction Confidence Factors. Proceedings of the 20th International Conference on Web Information Systems and Technologies (WEBIST 24).
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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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2022. Persona Finetuning for Online Gaming using Personalisation Techniques. Proceedings of the 2022 HCII Conference.
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2022. On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets. Information. 13:302.
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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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2021. Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering. Applied Sciences. 11:8369.
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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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2020. An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings. Algorithms. 13:174.
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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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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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2020. What makes a review a reliable rating in recommender systems? Information Processing & Management. 57(6):102304.
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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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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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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).