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        <title>Lot or Not: Identifying Multi-Quantity Offerings in E-Commerce</title>
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            <title>Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)</title>
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        <name type="personal">
            <namePart type="given">Shervin</namePart>
            <namePart type="family">Malmasi</namePart>
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        <name type="personal">
            <namePart type="given">Oleg</namePart>
            <namePart type="family">Rokhlenko</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nicola</namePart>
            <namePart type="family">Ueffing</namePart>
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        <name type="personal">
            <namePart type="given">Eugene</namePart>
            <namePart type="family">Agichtein</namePart>
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        <name type="personal">
            <namePart type="given">Surya</namePart>
            <namePart type="family">Kallumadi</namePart>
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    <abstract>The term lot in is defined to mean an offering that contains a collection of multiple identical items for sale. In a large online marketplace, lot offerings play an important role, allowing buyers and sellers to set price levels to optimally balance supply and demand needs. In spite of their central role, platforms often struggle to identify lot offerings, since explicit lot status identification is frequently not provided by sellers. The ability to identify lot offerings plays a key role in many fundamental tasks, from matching offerings to catalog products, through ranking search results, to providing effective pricing guidance. In this work, we seek to determine the lot status (and lot size) of each offering, in order to facilitate an improved buyer experience, while reducing the friction for sellers posting new offerings. We demonstrate experimentally the ability to accurately classify offerings as lots and predict their lot size using only the offer title, by adapting state-of-the-art natural language techniques to the lot identification problem.</abstract>
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    <identifier type="doi">10.18653/v1/2022.ecnlp-1.29</identifier>
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%0 Conference Proceedings
%T Lot or Not: Identifying Multi-Quantity Offerings in E-Commerce
%A Lavee, Gal
%A Guy, Ido
%Y Malmasi, Shervin
%Y Rokhlenko, Oleg
%Y Ueffing, Nicola
%Y Guy, Ido
%Y Agichtein, Eugene
%Y Kallumadi, Surya
%S Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F lavee-guy-2022-lot
%X The term lot in is defined to mean an offering that contains a collection of multiple identical items for sale. In a large online marketplace, lot offerings play an important role, allowing buyers and sellers to set price levels to optimally balance supply and demand needs. In spite of their central role, platforms often struggle to identify lot offerings, since explicit lot status identification is frequently not provided by sellers. The ability to identify lot offerings plays a key role in many fundamental tasks, from matching offerings to catalog products, through ranking search results, to providing effective pricing guidance. In this work, we seek to determine the lot status (and lot size) of each offering, in order to facilitate an improved buyer experience, while reducing the friction for sellers posting new offerings. We demonstrate experimentally the ability to accurately classify offerings as lots and predict their lot size using only the offer title, by adapting state-of-the-art natural language techniques to the lot identification problem.
%R 10.18653/v1/2022.ecnlp-1.29
%U https://aclanthology.org/2022.ecnlp-1.29
%U https://doi.org/10.18653/v1/2022.ecnlp-1.29
%P 250-262

Markdown (Informal)

[Lot or Not: Identifying Multi-Quantity Offerings in E-Commerce](https://aclanthology.org/2022.ecnlp-1.29) (Lavee & Guy, ECNLP 2022)

• Lot or Not: Identifying Multi-Quantity Offerings in E-Commerce (Lavee & Guy, ECNLP 2022)

ACL

• Gal Lavee and Ido Guy. 2022. Lot or Not: Identifying Multi-Quantity Offerings in E-Commerce. In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 250–262, Dublin, Ireland. Association for Computational Linguistics.