Adapting information retrieval to user signals via stochastic models

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

To address the challenge of adapting Information Retrieval (IR) to the constantly evolving user tasks and needs and to adjust it to user interactions and preferences we develop a new model of user behavior based onMarkov chains. We aim at integrating the proposed model into several aspects of IR, i.e. evaluation measures, systems and collections. Firstly, we studied IR evaluation measures and we propose a theoret- ical framework to describe their properties. Then, we pre- sented a new family of evaluation measures, called Markov Precision (MP), based on the proposed model and able to explicitly link lab-style and on-line evaluation metrics. Fu- ture work will include the presented model into Learning to Rank (LtR) algorithms and will define a collection for evaluation and comparison of Personalized Information Re- trieval (PIR) systems.

TitelWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
Antal sider1
ForlagAssociation for Computing Machinery, Inc.
Publikationsdato2 feb. 2017
ISBN (Elektronisk)9781450346757
StatusUdgivet - 2 feb. 2017
Eksternt udgivetJa
Begivenhed10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, Storbritannien
Varighed: 6 feb. 201710 feb. 2017


Konference10th ACM International Conference on Web Search and Data Mining, WSDM 2017
SponsorACM SIGKDD, ACM SIGMOD, ACM SIGWEB, Special Interest Group on Information Retrieval (ACM SIGIR)

ID: 216517423