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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Vedula, N., Collins, M., Agichtein, E., Rokhlenko, O. (2022). What Matters for Shoppers: Investigating Key Attributes for Online Product Comparison. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_27
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DOI: https://doi.org/10.1007/978-3-030-99739-7_27
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