@inproceedings{chia-etal-2022-come,
title = "{``}Does it come in black?{''} {CLIP}-like models are zero-shot recommenders",
author = "Chia, Patrick John and
Tagliabue, Jacopo and
Bianchi, Federico and
Greco, Ciro and
Goncalves, Diogo",
editor = "Malmasi, Shervin and
Rokhlenko, Oleg and
Ueffing, Nicola and
Guy, Ido and
Agichtein, Eugene and
Kallumadi, Surya",
booktitle = "Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ecnlp-1.22",
doi = "10.18653/v1/2022.ecnlp-1.22",
pages = "191--198",
abstract = "Product discovery is a crucial component for online shopping. However, item-to-item recommendations today do not allow users to explore changes along selected dimensions: given a query item, can a model suggest something similar but in a different color? We consider item recommendations of the comparative nature (e.g. {``}something darker{''}) and show how CLIP-based models can support this use case in a zero-shot manner. Leveraging a large model built for fashion, we introduce GradREC and its industry potential, and offer a first rounded assessment of its strength and weaknesses.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chia-etal-2022-come">
<titleInfo>
<title>“Does it come in black?” CLIP-like models are zero-shot recommenders</title>
</titleInfo>
<name type="personal">
<namePart type="given">Patrick</namePart>
<namePart type="given">John</namePart>
<namePart type="family">Chia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jacopo</namePart>
<namePart type="family">Tagliabue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Federico</namePart>
<namePart type="family">Bianchi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ciro</namePart>
<namePart type="family">Greco</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Diogo</namePart>
<namePart type="family">Goncalves</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Shervin</namePart>
<namePart type="family">Malmasi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Oleg</namePart>
<namePart type="family">Rokhlenko</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nicola</namePart>
<namePart type="family">Ueffing</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ido</namePart>
<namePart type="family">Guy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eugene</namePart>
<namePart type="family">Agichtein</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Surya</namePart>
<namePart type="family">Kallumadi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Dublin, Ireland</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Product discovery is a crucial component for online shopping. However, item-to-item recommendations today do not allow users to explore changes along selected dimensions: given a query item, can a model suggest something similar but in a different color? We consider item recommendations of the comparative nature (e.g. “something darker”) and show how CLIP-based models can support this use case in a zero-shot manner. Leveraging a large model built for fashion, we introduce GradREC and its industry potential, and offer a first rounded assessment of its strength and weaknesses.</abstract>
<identifier type="citekey">chia-etal-2022-come</identifier>
<identifier type="doi">10.18653/v1/2022.ecnlp-1.22</identifier>
<location>
<url>https://aclanthology.org/2022.ecnlp-1.22</url>
</location>
<part>
<date>2022-05</date>
<extent unit="page">
<start>191</start>
<end>198</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T “Does it come in black?” CLIP-like models are zero-shot recommenders
%A Chia, Patrick John
%A Tagliabue, Jacopo
%A Bianchi, Federico
%A Greco, Ciro
%A Goncalves, Diogo
%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 chia-etal-2022-come
%X Product discovery is a crucial component for online shopping. However, item-to-item recommendations today do not allow users to explore changes along selected dimensions: given a query item, can a model suggest something similar but in a different color? We consider item recommendations of the comparative nature (e.g. “something darker”) and show how CLIP-based models can support this use case in a zero-shot manner. Leveraging a large model built for fashion, we introduce GradREC and its industry potential, and offer a first rounded assessment of its strength and weaknesses.
%R 10.18653/v1/2022.ecnlp-1.22
%U https://aclanthology.org/2022.ecnlp-1.22
%U https://doi.org/10.18653/v1/2022.ecnlp-1.22
%P 191-198
Markdown (Informal)
[“Does it come in black?” CLIP-like models are zero-shot recommenders](https://aclanthology.org/2022.ecnlp-1.22) (Chia et al., ECNLP 2022)
ACL