Predicting Diverse Subsets Using Structural SVMs
published: Aug. 4, 2008, recorded: July 2008, views: 397
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In many retrieval tasks, one important goal involves retrieving a diverse set of results (e.g., documents covering a wide range of topics for a search query). First of all, this reduces redundancy, effectively presenting more information with the presented results. Secondly, search queries are often ambiguous at some level. For example, the query “Jaguar” can refer to many different topics (such as the car or the feline). A set of documents with high topic diversity ensures that fewer users abandon the query because none of the results are relevant to them. Unlike existing approaches to learning retrieval functions, we present a method that explicitly trains to diversify results. In particular, we formulate the learning problem of predicting a diverse subset and derive a training algorithm based on structural SVMs.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !