Search Engines that Learn Online

Publication Type  Conference Paper
Author  Hofmann K.
Year of Publication  2011
Conference Name  34th Annual International ACM SIGIR Conference (SIGIR 2011)
Pagination  1313--1314
Month Published  July
Publisher  ACM
Conference Location  Beijing
Abstract  

The goal of my research is to develop self-learning search engines, that can learn online, i.e., directly from interactions with actual users. Such systems can continuously adapt to user preferences throughout their lifetime, leading to better search performance in settings where expensive manual tuning is infeasible. Challenges that are addressed in my work include the development of effective online learning to rank algorithms for IR, user aspects, and evaluation.

Export  BibTex
Full paper  PDF (65.13 KB)
AttachmentSize
sigir2011-dc-hofmann.pdf65.13 KB