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.
%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 recommendation tag usage
%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 = {2017-12-23T13:08:05.000+0100},
author = {Zoller, Daniel and Doerfel, Stephan and Pölitz, Christian and Hotho, Andreas},
biburl = {https://www.bibsonomy.org/bibtex/22d8fcb8ac8112dbf5aca43d8c961b7ef/thoni},
booktitle = {Proceedings of the Workshop on Temporal Reasoning in Recommender Systems},
interhash = {8475d49373f81341b05682ee6e0146a9},
intrahash = {2d8fcb8ac8112dbf5aca43d8c961b7ef},
keywords = {recommendation tag usage},
series = {{CEUR} Workshop Proceedings},
timestamp = {2017-12-23T13:08:05.000+0100},
title = {Leveraging User-Interactions for Time-Aware Tag Recommendations},
year = 2017
}