@inproceedings{takayanagi-etal-2024-frogs,
title = "Frogs into princes: A generative model to understand the success of product descriptions",
author = "Takayanagi, Takehiro and
Charron, Bruno and
Visentini-Scarzanella, Marco",
editor = "Malmasi, Shervin and
Fetahu, Besnik and
Ueffing, Nicola and
Rokhlenko, Oleg and
Agichtein, Eugene and
Guy, Ido",
booktitle = "Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.ecnlp-1.10",
pages = "88--96",
abstract = "In the dynamic marketplace, vendors continuously seek innovative ideas for new products and ways to improve existing ones. These ideas can be uncovered by analyzing text data, such as product descriptions and customer reviews. However, the ever-increasing volume of text data poses a challenge in extracting meaningful insights. Therefore, this study addresses the challenge of extracting actionable insights from the growing volume of text data, with a specific focus on product descriptions. To this end, we investigate two primary research questions: the predictive power of product descriptions for product success, and the capability of style transfer to highlight the successful factors of these descriptions. In response to the first question, our findings validate that product descriptions are indeed reliable indicators of product success. Addressing our second question, we propose a Successful Style Transfer Variational Autoencoder (SST-VAE), a VAE-based language model designed for effective successful style transfer. Qualitative analysis indicates that the SST-VAE effectively enables successful style transfer conditional on a given label. In addition, case studies suggest that the proposed approach could be useful in gaining insights about product success, by highlighting key factors that may contribute to their success. On the other hand, our approach confronts issues such as hallucinations and the need for factual accuracy. These challenges underscore the necessity for continued research in the field of e-commerce natural language processing.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="takayanagi-etal-2024-frogs">
<titleInfo>
<title>Frogs into princes: A generative model to understand the success of product descriptions</title>
</titleInfo>
<name type="personal">
<namePart type="given">Takehiro</namePart>
<namePart type="family">Takayanagi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bruno</namePart>
<namePart type="family">Charron</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marco</namePart>
<namePart type="family">Visentini-Scarzanella</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024</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">Besnik</namePart>
<namePart type="family">Fetahu</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">Oleg</namePart>
<namePart type="family">Rokhlenko</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">Ido</namePart>
<namePart type="family">Guy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In the dynamic marketplace, vendors continuously seek innovative ideas for new products and ways to improve existing ones. These ideas can be uncovered by analyzing text data, such as product descriptions and customer reviews. However, the ever-increasing volume of text data poses a challenge in extracting meaningful insights. Therefore, this study addresses the challenge of extracting actionable insights from the growing volume of text data, with a specific focus on product descriptions. To this end, we investigate two primary research questions: the predictive power of product descriptions for product success, and the capability of style transfer to highlight the successful factors of these descriptions. In response to the first question, our findings validate that product descriptions are indeed reliable indicators of product success. Addressing our second question, we propose a Successful Style Transfer Variational Autoencoder (SST-VAE), a VAE-based language model designed for effective successful style transfer. Qualitative analysis indicates that the SST-VAE effectively enables successful style transfer conditional on a given label. In addition, case studies suggest that the proposed approach could be useful in gaining insights about product success, by highlighting key factors that may contribute to their success. On the other hand, our approach confronts issues such as hallucinations and the need for factual accuracy. These challenges underscore the necessity for continued research in the field of e-commerce natural language processing.</abstract>
<identifier type="citekey">takayanagi-etal-2024-frogs</identifier>
<location>
<url>https://aclanthology.org/2024.ecnlp-1.10</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>88</start>
<end>96</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Frogs into princes: A generative model to understand the success of product descriptions
%A Takayanagi, Takehiro
%A Charron, Bruno
%A Visentini-Scarzanella, Marco
%Y Malmasi, Shervin
%Y Fetahu, Besnik
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the Seventh Workshop on e-Commerce and NLP @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F takayanagi-etal-2024-frogs
%X In the dynamic marketplace, vendors continuously seek innovative ideas for new products and ways to improve existing ones. These ideas can be uncovered by analyzing text data, such as product descriptions and customer reviews. However, the ever-increasing volume of text data poses a challenge in extracting meaningful insights. Therefore, this study addresses the challenge of extracting actionable insights from the growing volume of text data, with a specific focus on product descriptions. To this end, we investigate two primary research questions: the predictive power of product descriptions for product success, and the capability of style transfer to highlight the successful factors of these descriptions. In response to the first question, our findings validate that product descriptions are indeed reliable indicators of product success. Addressing our second question, we propose a Successful Style Transfer Variational Autoencoder (SST-VAE), a VAE-based language model designed for effective successful style transfer. Qualitative analysis indicates that the SST-VAE effectively enables successful style transfer conditional on a given label. In addition, case studies suggest that the proposed approach could be useful in gaining insights about product success, by highlighting key factors that may contribute to their success. On the other hand, our approach confronts issues such as hallucinations and the need for factual accuracy. These challenges underscore the necessity for continued research in the field of e-commerce natural language processing.
%U https://aclanthology.org/2024.ecnlp-1.10
%P 88-96
Markdown (Informal)
[Frogs into princes: A generative model to understand the success of product descriptions](https://aclanthology.org/2024.ecnlp-1.10) (Takayanagi et al., ECNLP-WS 2024)
ACL