NDLI: Structured query reformulations in commerce search

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Structured query reformulations in commerce search

Content Provider	ACM Digital Library
Author	Gollapudi, Sreenivas Ieong, Samuel Kannan, Anitha
Abstract	Recent work in commerce search has shown that understanding the semantics in user queries enables more effective query analysis and retrieval of relevant products. However, due to lack of sufficient domain knowledge, user queries often include terms that cannot be mapped directly to any product attribute. For example, a user looking for designer handbags might start with such a query because she is not familiar with the manufacturers, the price ranges, and/or the material that gives a handbag designer appeal. Current commerce search engines treat terms such as designer as keywords and attempt to match them to contents such as product reviews and product descriptions, often resulting in poor user experience. In this study, we propose to address this problem by reformulating queries involving terms such as designer, which we call modifiers, to queries that specify precise product attributes. We learn to rewrite the modifiers to attribute values by analyzing user behavior and leveraging structured data sources such as the product catalog that serves the queries. We first produce a probabilistic mapping between the modifiers and attribute values based on user behavioral data. These initial associations are then used to retrieve products from the catalog, over which we infer sets of attribute values that best describe the semantics of the modifiers. We evaluate the effectiveness of our approach based on a comprehensive Mechanical Turk study. We find that users agree with the attribute values selected by our approach in about 95% of the cases and they prefer the results surfaced for our reformulated queries to ones for the original queries in 87% of the time.
Starting Page	1890
Ending Page	1894
Page Count	5
File Format	PDF
ISBN	9781450311564
DOI	10.1145/2396761.2398538
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Structured search
Content Type	Text
Resource Type	Article

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