Semantic Web Dog Food

Presented at: 20th International World Wide Web Conference (WWW2011)

by Beibei Li, Anindya Ghose, Panagiotis G. Ipeirotis

Most product search engines today build on models of relevance devised for information retrieval. However, the decision mechanism that underlies the process of buying a product is different than the process of locating relevant documents or objects. We propose a theory model for product search based on expected utility theory from economics. Specifically, we propose a ranking technique in which we rank highest the products that generate the highest surplus, after the purchase. We instantiate our research by building a demo search engine for hotels that takes into account consumer heterogeneous preferences, and also accounts for the varying hotel price. Moreover, we achieve this without explicitly asking the preferences or purchasing histories of individual consumers but by using aggregate demand data. This new ranking system is able to recommend consumers products with ``best value for money'' in a privacy-preserving manner. The demo is accessible at http://nyuhotels.appspot.com/

A Demo Search Engine for Products was presented at this event.

Keywords: World Wide Web

Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2011/demo/a-demo-search-engine-for-products

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