Learning Image Information for eCommerce Queries
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Computing similarity between a query and a document is fundamental in any information retrieval system. In search engines, computing query-document similarity is an essential step in both retrieval and ranking stages. In eBay search, document is an item and the query-item similarity can be computed by comparing different facets of the query-item pair. Query text can be compared with the text of the item title. Likewise, a category constraint applied on the query can be compared with the listing category of the item. However, images are one signal that are usually present in the items but are not present in the query. Images are one of the most intuitive signals used by users to determine the relevance of the item given a query. Including this signal in estimating similarity between the query-item pair is likely to improve the relevance of the search engine. We propose a novel way of deriving image information for queries. We attempt to learn image information for queries from item images instead of generating explicit image features or an image for queries. We use canonical correlation analysis (CCA) to learn a new subspace where projecting the original data will give us a new query and item representation. We hypothesize that this new query representation will also have image information about the query. We estimate the query-item similarity using a vector space model and report the performance of the proposed method on eBay's search data. We show 11.89% relevance improvement over the baseline using area under the receiver operating characteristic curve (AUROC) as the evaluation metric. We also show 3.1% relevance improvement over the baseline with area under the precision recall curve (AUPRC) .
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