%0 Conference Paper %1 zoller2017leveraging %A Zoller, Daniel %A Doerfel, Stephan %A Pölitz, Christian %A Hotho, Andreas %B Proceedings of the Workshop on Temporal Reasoning in Recommender Systems %D 2017 %K %T Leveraging User-Interactions for Time-Aware Tag Recommendations %X For the popular task of tag recommendation, various (complex) approaches have been proposed. Recently however, research has focused on heuristics with low computational effort and particularly, a time-aware heuristic, called BLL, has been shown to compare well to various state-of-the-art methods. Here, we follow up on these results by presenting another time-aware approach leveraging user interaction data in an easily interpretable, on-the-fly computable approach that can successfully be combined with BLL. We investigate the influence of time as a parameter in that approach, and we demonstrate the effectiveness of the proposed method using two datasets from the popular public social tagging system BibSonomy.

@inproceedings{zoller2017leveraging, abstract = {For the popular task of tag recommendation, various (complex) approaches have been proposed. Recently however, research has focused on heuristics with low computational effort and particularly, a time-aware heuristic, called BLL, has been shown to compare well to various state-of-the-art methods. Here, we follow up on these results by presenting another time-aware approach leveraging user interaction data in an easily interpretable, on-the-fly computable approach that can successfully be combined with BLL. We investigate the influence of time as a parameter in that approach, and we demonstrate the effectiveness of the proposed method using two datasets from the popular public social tagging system BibSonomy.}, added-at = {2017-11-07T09:14:56.000+0100}, author = {Zoller, Daniel and Doerfel, Stephan and Pölitz, Christian and Hotho, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/22d8fcb8ac8112dbf5aca43d8c961b7ef/nosebrain}, booktitle = {Proceedings of the Workshop on Temporal Reasoning in Recommender Systems}, interhash = {8475d49373f81341b05682ee6e0146a9}, intrahash = {2d8fcb8ac8112dbf5aca43d8c961b7ef}, keywords = {}, series = {{CEUR} Workshop Proceedings}, timestamp = {2017-11-07T09:14:56.000+0100}, title = {Leveraging User-Interactions for Time-Aware Tag Recommendations}, year = 2017 }