@inproceedings{blaz2023,

title = {Recommendations in Visual Analytics - An Analytical Approach for Elaboration in Science},

author = {Midhad Blazevic and Lennart B. Sina and Cristian A. Secco and Kawa Nazemi},

doi = {10.1109/IV60283.2023.00052},

year  = {2023},

date = {2023-11-24},

urldate = {2023-11-24},

booktitle = {Proceedings of the 27th International Conference Information Visualisation (IV 2023)},

pages = {259- 267},

publisher = {IEEE CPS},

abstract = {The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using recommendations in various application domains, the full potential of recommendation systems is not yet fully utilized. Particularly, there are missing approaches that combine interactive visualizations with recommendation systems to enable an analytical investigation of the current state of technology and science. We, therefore, propose in this work a novel Visual Analytics approach that integrates recommendation methods as the model and provides a seamless integration of both interactive visualizations and recommendation systems. We utilize MAE and RMSE metrics and human validation to identify the best approach out of eight approaches that differ in vectorization and similarity algorithms to recommend scientific items. We contribute novel approaches for recommending scientific publications, venues, and projects, based on comparing traditional and deep-learning-based recommendation approaches. Furthermore, we propose a Visual Analytics approach that uses recommendation methods for analytical elaboration. This work shows the potential of integrating recommendation systems into scientific research and identifies potential future directions for improving the proposed model.},

keywords = {Artifical Intelligence},

pubstate = {published},

tppubtype = {inproceedings}

}