Predicting Diverse Subsets Using Structural SVMs
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 showing more information with the presented results. Secondly, queries are often ambiguous at some level. For example, the query "Jaguar" can refer to many different topics (such as the car or feline). A set of documents with high topic diversity ensures that fewer users abandon the query because no 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 diverse subsets and derive a training method based on structural SVMs.
© 2014 ACM, Inc. The work was funded under NSF Award IIS-0713483, NSF CAREER Award 0237381, and a gift from Yahoo! Research. The first author is also partly funded by a Microsoft Research Fellowship and a Yahoo! Key Technical Challenge Grant. The authors also thank Darko Kirovski for initial discussions regarding his work on Essential Pages.