While there are many commercial systems to help people browse and compare products, these interfaces are typically  product  centric. To help users identify products that match their needs more efficiently, we instead focus on building a task centric interface and system. Based on answers to initial questions about the situations in which they expect to use the product, the interface identifies products that match their needs, and exposes high-level product features related to their tasks, as well as low-level information including customer reviews and product specifications. We developed semi-automatic methods to extract the high-level features used by the system from online product data. These methods identify and group product features, mine and summarize opinions about those features, and identify product uses. User studies verified our focus on high-level features for browsing products and low-level features and specifications for comparing products.
['Scott Carter', 'Francine Chen', 'Aditi S. Muralidharan', 'Jeremy Pickens']
DiG: a task-based approach to product search