Dynamic Content Based Ranking
Abstract
We introduce a novel state space model for a set of sequentially time-stamped partial rankings of items and textual descriptions for the items. Based on the data, the model infers text-based themes that are predictive of the rankings enabling forecasting tasks and performing trend analysis. We propose a scaled Gamma process based prior for capturing the underlying dynamics. Based on two challenging and contemporary real data collections, we show the model infers meaningful and useful textual themes as well as performs better than existing related dynamic models.
Cite
Text
Virtanen and Girolami. "Dynamic Content Based Ranking." Artificial Intelligence and Statistics, 2020.Markdown
[Virtanen and Girolami. "Dynamic Content Based Ranking." Artificial Intelligence and Statistics, 2020.](https://mlanthology.org/aistats/2020/virtanen2020aistats-dynamic/)BibTeX
@inproceedings{virtanen2020aistats-dynamic,
title = {{Dynamic Content Based Ranking}},
author = {Virtanen, Seppo and Girolami, Mark},
booktitle = {Artificial Intelligence and Statistics},
year = {2020},
pages = {2315-2324},
volume = {108},
url = {https://mlanthology.org/aistats/2020/virtanen2020aistats-dynamic/}
}