PROCAT: Product Catalogue Dataset for Implicit Clustering, Permutation Learning and Structure Prediction

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Abstrakt

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.
OriginalsprogEngelsk
TitelThirty-fifth Conference on Neural Information Processing Systems : Datasets and Benchmarks Track
Vol/bind1
Publikationsdato1 dec. 2021
Udgave2021
DOI
StatusUdgivet - 1 dec. 2021
BegivenhedThirty-fifth Conference on Neural Information Processing Systems - Virtual
Varighed: 6 dec. 202114 dec. 2021
Konferencens nummer: 25
https://nips.cc/

Konference

KonferenceThirty-fifth Conference on Neural Information Processing Systems
Nummer25
LokationVirtual
Periode06/12/202114/12/2021
Internetadresse

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