AISTATS 2020poster0 citations
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.
BibTeX
@InProceedings{pmlr-v108-virtanen20a,
title = {Dynamic content based ranking},
author = {Virtanen, Seppo and Girolami, Mark},
booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics},
pages = {2315--2324},
year = {2020},
editor = {Chiappa, Silvia and Calandra, Roberto},
volume = {108},
series = {Proceedings of Machine Learning Research},
month = {26--28 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v108/virtanen20a/virtanen20a.pdf},
url = {https://proceedings.mlr.press/v108/virtanen20a.html},
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.}
}