Dynamic content based ranking

Seppo Virtanen, Mark Girolami
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:2315-2324, 2020.

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 this Paper

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.} }
Endnote
%0 Conference Paper %T Dynamic content based ranking %A Seppo Virtanen %A Mark Girolami %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-virtanen20a %I PMLR %P 2315--2324 %U https://proceedings.mlr.press/v108/virtanen20a.html %V 108 %X 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.
APA
Virtanen, S. & Girolami, M.. (2020). Dynamic content based ranking. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:2315-2324 Available from https://proceedings.mlr.press/v108/virtanen20a.html.

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