Leveraging User-Interactions for Time-Aware Tag Recommendations
D. Zoller, S. Doerfel, C. Pölitz, and A. Hotho. Proceedings of the 1st Workshop on Temporal Reasoning in Recommender Systems co-located with 11th International Conference on Recommender Systems (RecSys 2017), CEUR-WS, (2017)
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.
Proceedings of the 1st Workshop on Temporal Reasoning in Recommender Systems co-located with 11th International Conference on Recommender Systems (RecSys 2017)
%0 Conference Paper
%1 zoller2017leveraging
%A Zoller, Daniel
%A Doerfel, Stephan
%A Pölitz, Christian
%A Hotho, Andreas
%B Proceedings of the 1st Workshop on Temporal Reasoning in Recommender Systems co-located with 11th International Conference on Recommender Systems (RecSys 2017)
%D 2017
%E Bielikova, Maria
%E Bogina, Veronika
%E Kuflik, Tsvi
%E Sasson, Roy
%I CEUR-WS
%K 2017 interaction kde myown selected
%T Leveraging User-Interactions for Time-Aware Tag Recommendations
%U http://ceur-ws.org/Vol-1922/
%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 = {2018-01-22T23:14:49.000+0100},
author = {Zoller, Daniel and Doerfel, Stephan and Pölitz, Christian and Hotho, Andreas},
biburl = {https://www.bibsonomy.org/bibtex/2563361c982b1fc51c1633c32a732f22e/sdo},
booktitle = {Proceedings of the 1st Workshop on Temporal Reasoning in Recommender Systems co-located with 11th International Conference on Recommender Systems (RecSys 2017)},
editor = {Bielikova, Maria and Bogina, Veronika and Kuflik, Tsvi and Sasson, Roy},
interhash = {8475d49373f81341b05682ee6e0146a9},
intrahash = {563361c982b1fc51c1633c32a732f22e},
keywords = {2017 interaction kde myown selected},
publisher = {CEUR-WS},
series = {{CEUR} Workshop Proceedings},
timestamp = {2018-02-10T16:12:53.000+0100},
title = {Leveraging User-Interactions for Time-Aware Tag Recommendations},
url = {http://ceur-ws.org/Vol-1922/},
year = 2017
}