@article {853, title = {Cost-Efficient RSSI-Based Indoor Proximity Positioning, for Large/Complex Museum Exhibition Spaces}, journal = {Sensors}, volume = {25}, year = {2025}, pages = {2713}, abstract = {

RSSI-based proximity positioning is a well-established technique for indoor localization, featuring simplicity and cost-effectiveness, requiring low-price and off-the-shelf hardware. However, it suffers from low accuracy (in NLOS traffic), noise, and multipath fading issues. In large complex spaces, such as museums, where heavy visitor traffic is expected to seriously impact the ability to maintain LOS, RSSI coupled with Bluetooth Low Energy (BLE) seems ideal in terms of market availability, cost-/energy-efficiency and scalability that affect competing technologies, provided it achieves adequate accuracy. Our work reports and discusses findings of a BLE/RSSI-based pilot, implemented at the Museum of Modern Greek Culture in Athens, involving eight buildings with 47 halls with diverse areas, shapes, and showcase layouts. Wearable visitor BLE beacons provided cell-level location determined by a prototype tool (VTT), integrating in its architecture different functionalities: raw RSSI data smoothing with Kalman filters, hybrid positioning provision, temporal methods for visitor cell prediction, spatial filtering, and prediction based on popular machine learning classifiers. Visitor movement modeling, based on critical parameters influencing signal measurements, provided scenarios mapped to popular behavioral models. One such model, {\textquotedblleft}ant{\textquotedblright}, corresponding to relatively slow nomadic cell roaming, was selected for basic experimentation. Pilot implementation decisions and methods adopted at all layers of the VTT architecture followed the overall concept of simplicity, availability, and cost-efficiency, providing a maximum infrastructure cost of 8 Euro per m2 covered. A total 15 methods/algorithms were evaluated against prediction accuracy across 20 RSSI datasets, incorporating diverse hall cell allocations and visitor movement patterns. RSSI data, temporal and spatial management with simple low-processing methods adopted, achieved a maximum prediction accuracy average of 81.53\% across all datasets, while ML algorithms (Random Forest) achieved a maximum prediction accuracy average of 87.24\%.

}, keywords = {Bluetooth Low Energy, indoor proximity positioning, large/complex museums, machine learning classification, museum visitor modeling, RSSI temporal/spatial methods}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s25092713}, url = {https://doi.org/10.3390/s25092713}, author = {Philippopoulos, Panos I. and Koutrakis, Kostas N. and Tsafaras, Efstathios D. and Papadopoulou, Evangelia G. and Sigalas, Dimitrios and Tselikas, Nikolaos D. and Ougiaroglou, Stefanos and Costas Vassilakis} } @conference {839, title = {Improving Recommendation Quality in Collaborative Filtering by Including Prediction Confidence Factors}, booktitle = {Proceedings of the 20th International Conference on Web Information Systems and Technologies (WEBIST 24)}, year = {2024}, publisher = {SCITEPRESS Digital Library}, organization = {SCITEPRESS Digital Library}, abstract = {

Collaborative filtering is a prevalent recommender system technique which generates rating predictions based on the rating values given by the users{\textquoteright} near neighbours. Consequently, for each user, the items scoring the highest prediction values are recommended to them. Unfortunately, predictions inherently entail errors, which, in the case of recommender systems, manifest as unsuccessful recommendations. However, along with each rating prediction value, prediction confidence factors can be computed. As a result, items having low prediction confidence factor values, can be either declined for recommendation or have their recommendation priority demoted. In the former case, some users may receive fewer recommended items or even none, especially when using a sparse dataset. In this paper, we present an algorithm that determines the items to be recommended by considering both the rating prediction values and confidence factors of predictions, allowing for predictions with higher confidence factors to outrank predictions with higher value, but lower confidence. The presented algorithm achieves to enhance the recommendation quality, while at the same time retaining the number of recommendations for each user.

}, keywords = {Algorithm, Certainty factors, collaborative filtering, Rating Predictions, Recommender Systems}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis and Stefanos Ougiaroglou} } @article {820, title = {Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets}, journal = {Big Data and Cognitive Computing}, volume = {7}, year = {2023}, month = {mar}, pages = {59}, abstract = {

Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered {\textquotedblleft}close{\textquotedblright} to the user for whom the prediction is being generated have given to the same product or service. In general, {\textquotedblleft}close{\textquotedblright} users for some user u correspond to users that have rated items similarly to u and these users are termed as {\textquotedblleft}near neighbors{\textquotedblright}. As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset{\textquoteright}s density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method{\textquoteright}s efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets.

}, keywords = {collaborative filtering, Evaluation, low density datasets, personalization, rating prediction quality, Recommender Systems, reliability}, doi = {10.3390/bdcc7020059}, url = {https://www.mdpi.com/2504-2289/7/2/59}, author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos and Stefanos Ougiaroglou} } @book {DBLP:reference/db/2018, title = {Encyclopedia of Database Systems, Second Edition}, year = {2018}, publisher = {Springer}, organization = {Springer}, isbn = {978-1-4614-8266-6}, doi = {10.1007/978-1-4614-8265-9}, url = {https://doi.org/10.1007/978-1-4614-8265-9}, editor = {Ling Liu and M. Tamer {\"O}zsu} } @book {DBLP:reference/db/2009, title = {Encyclopedia of Database Systems}, year = {2009}, publisher = {Springer US}, organization = {Springer US}, isbn = {978-0-387-35544-3}, doi = {10.1007/978-0-387-39940-9}, url = {https://doi.org/10.1007/978-0-387-39940-9}, editor = {Ling Liu and M. Tamer {\"O}zsu} } @proceedings {DBLP:conf/icde/2007, title = {Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, The Marmara Hotel, Istanbul, Turkey, April 15-20, 2007}, year = {2007}, publisher = {IEEE Computer Society}, isbn = {1-4244-0802-4}, url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4221634}, editor = {Rada Chirkova and Asuman Dogac and M. Tamer {\"O}zsu and Timos K. Sellis} } @proceedings {DBLP:conf/caise/2002, title = {Advanced Information Systems Engineering, 14th International Conference, CAiSE 2002, Toronto, Canada, May 27-31, 2002, Proceedings}, volume = {2348}, year = {2002}, publisher = {Springer}, isbn = {3-540-43738-X}, doi = {10.1007/3-540-47961-9}, url = {https://doi.org/10.1007/3-540-47961-9}, editor = {Anne Banks Pidduck and John Mylopoulos and Carson C. Woo and M. Tamer {\"O}zsu} }