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Erschienen in:
Improving BERT-based Query-by-Document Retrieval with Multi-task Optimization Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

What Matters for Shoppers: Investigating Key Attributes for Online Product Comparison

verfasst von : Nikhita Vedula, Marcus Collins, Eugene Agichtein, Oleg Rokhlenko

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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Abstract

Before making high-consideration purchase decisions, shoppers generally need to identify and evaluate products’ key differentiating features or attributes. Many customers, however, lack the knowledge required to do so for all product domains. In this work, we investigate and analyze alternatives for identifying important product attributes, which customers can then use to compare candidate products. We propose an unsupervised attribute-ranking approach ReBARC, that combines both objective data from structured product catalogs, and subjective information from unstructured customer reviews, to suggest to the shopper the most important attributes to consider. Our detailed analysis of product attribute importance across various domains on a shopping website shows that ReBARC significantly outperforms prior efforts judged by both automated and human evaluation metrics. We also analyze the correlation and overlap between key product attributes detected by ReBARC, and those visible to customers during online product search.

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Vorheriges Kapitel Zero-Shot Recommendation as Language Modeling
Nächstes Kapitel Evaluating Simulated User Interaction and Search Behaviour
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Metadaten
Titel
What Matters for Shoppers: Investigating Key Attributes for Online Product Comparison
verfasst von
Nikhita Vedula
Marcus Collins
Eugene Agichtein
Oleg Rokhlenko
Copyright-Jahr
2022
Buch
Advances in Information Retrieval

Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-99739-7_27