TY - CHAP

T1 - Learning to rank user intent

AU - Giannopoulos, Giorgos

AU - Brefeld, Ulf

AU - Dalamagas, Theodore

AU - Sellis, Timos

PY - 2011

Y1 - 2011

N2 - Personalized retrieval models aim at capturing user interests to provide personalized results that are tailored to the respective information needs. User interests are however widely spread, subject to change, and cannot always be captured well, thus rendering the deployment of personalized models challenging. We take a different approach and study ranking models for user intent. We exploit user feedback in terms of click data to cluster ranking models for historic queries according to user behavior and intent. Each cluster is finally represented by a single ranking model that captures the contained search interests expressed by users. Once new queries are issued, these are mapped to the clustering and the retrieval process diversifies possible intents by combining relevant ranking functions. Empirical evidence shows that our approach significantly outperforms baseline approaches on a large corporate query log.

AB - Personalized retrieval models aim at capturing user interests to provide personalized results that are tailored to the respective information needs. User interests are however widely spread, subject to change, and cannot always be captured well, thus rendering the deployment of personalized models challenging. We take a different approach and study ranking models for user intent. We exploit user feedback in terms of click data to cluster ranking models for historic queries according to user behavior and intent. Each cluster is finally represented by a single ranking model that captures the contained search interests expressed by users. Once new queries are issued, these are mapped to the clustering and the retrieval process diversifies possible intents by combining relevant ranking functions. Empirical evidence shows that our approach significantly outperforms baseline approaches on a large corporate query log.

KW - Informatics

KW - Clickthrough data

KW - clustering

KW - ranking

KW - Relevance judgement

KW - Search behavior

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=83055168233&partnerID=8YFLogxK

U2 - 10.1145/2063576.2063609

DO - 10.1145/2063576.2063609

M3 - Article in conference proceedings

AN - SCOPUS:83055168233

SN - 978-1-4503-0717-8

SP - 195

EP - 200

BT - Proceedings of the 20th ACM international conference on Information and knowledge management

PB - Association for Computing Machinery, Inc

T2 - 20th ACM Conference on Information and Knowledge Management - CIKM '11

Y2 - 24 October 2011 through 28 October 2011

ER -