Abstract
In this dataset paper we introduce PROCAT, a novel e-commerce dataset containing expertly designed product catalogues consisting of individual product offers grouped into complementary sections. We aim to address the scarcity of existing datasets in the area of set-to-sequence machine learning tasks, which involve complex structure prediction. The task's difficulty is further compounded by the need to place into sequences rare and previously-unseen instances, as well as by variable sequence lengths and substructures, in the form of diversely-structured catalogues. PROCAT provides catalogue data consisting of over 1.5 million set items across a 4-year period, in both raw text form and with pre-processed features containing information about relative visual placement. In addition to this ready-to-use dataset, we include baseline experimental results on a proposed benchmark task from a number of joint set encoding and permutation learning model architectures.
Original language | English |
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Title of host publication | Thirty-fifth Conference on Neural Information Processing Systems : Datasets and Benchmarks Track |
Volume | 1 |
Publication date | 1 Dec 2021 |
Edition | 2021 |
DOIs | |
Publication status | Published - 1 Dec 2021 |
Event | Thirty-fifth Conference on Neural Information Processing Systems - Virtual Duration: 6 Dec 2021 → 14 Dec 2021 Conference number: 25 https://nips.cc/ |
Conference
Conference | Thirty-fifth Conference on Neural Information Processing Systems |
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Number | 25 |
Location | Virtual |
Period | 06/12/2021 → 14/12/2021 |
Internet address |
Keywords
- PROCAT
- E-commerce Dataset
- Set-to-Sequence Machine Learning
- Complex Structure Prediction
- Product Catalogues
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PROCAT Set-to-Sequence Model
Jurewicz, M. (Creator) & Derczynski, L. (Creator), ZENODO, 3 Jun 2021
DOI: 10.5281/zenodo.4896303, https://zenodo.org/record/4896303
Dataset