Semantic Web Dog Food

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

by Nish Parikh, Neel Sundaresan

The notion of relevance is key to the performance of search engines as they interpret the user queries and respond with matching results. Online search engines have used other features beyond pure IR features to return relevant matching documents. However, over-emphasis on relevance could lead to redundancy in search results. In document search, diversity is simply the variety of documents that span the result set. In an online marketplace the diversity in the result set is represented by items for sale by different sellers at different prices with different sales options. For such a marketplace, in order to minimize query abandonment and the risk of dissatisfaction to the average user, several factors like diversity, trust and value need to be taken into account. Previous work in this field [4] has shown an impossibility result that there exists no such function that can optimize for all these factors. Since these factors and the measures associated with the factors could be subjective we take an approach of giving the control back to the user. In this paper we describe an interface which enables users to have more control over the optimization function used to present the results. We demonstrate this for search on eBay - one of the largest online marketplaces with a vibrant user community and dynamic inventory. We use an algorithm based on bounded greedy selection [5] to construct the result set based on parameters specified by the user.

A User-Tunable Approach to Marketplace Search 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-user-tunable-approach-to-marketplace-search

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