  A high-quality, comprehensive product catalog is essential to the success of
Product Search engines and shopping sites such as Yahoo! Shopping, Google
Product Search or Bing Shopping. But keeping catalogs up-to-date becomes a
challenging task, calling for the need of automated techniques. In this paper,
we introduce the problem of product synthesis, a key component of catalog
creation and maintenance. Given a set of offers advertised by merchants, the
goal is to identify new products and add them to the catalog together with
their (structured) attributes. A fundamental challenge is the scale of the
problem: a Product Search engine receives data from thousands of merchants and
millions of products; the product taxonomy contains thousands of categories,
where each category comes in a different schema; and merchants use
representations for products that are different from the ones used in the
catalog of the Product Search engine.
  We propose a system that provides an end-to-end solution to the product
synthesis problem, and includes components for extraction, and addresses issues
involved in data extraction from offers, schema reconciliation, and data
fusion. We developed a novel and scalable technique for schema matching which
leverages knowledge about previously-known instance-level associations between
offers and products; and it is trained using automatically created training
sets (no manually-labeled data is needed). We present an experimental
evaluation of our system using data from Bing Shopping for more than 800K
offers, a thousand merchants, and 400 categories. The evaluation confirms that
our approach is able to automatically generate a large number of accurate
product specifications, and that our schema reconciliation component
outperforms state-of-the-art schema matching techniques in terms of precision
and recall.
