Adapting information retrieval to user signals via stochastic models

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Adapting information retrieval to user signals via stochastic models. / Maistro, Maria.

WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc., 2017.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Maistro, M 2017, Adapting information retrieval to user signals via stochastic models. in WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc., 10th ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, United Kingdom, 06/02/2017. https://doi.org/10.1145/3018661.3022753

APA

Maistro, M. (2017). Adapting information retrieval to user signals via stochastic models. In WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining Association for Computing Machinery, Inc.. https://doi.org/10.1145/3018661.3022753

Vancouver

Maistro M. Adapting information retrieval to user signals via stochastic models. In WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc. 2017 https://doi.org/10.1145/3018661.3022753

Author

Maistro, Maria. / Adapting information retrieval to user signals via stochastic models. WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc., 2017.

Bibtex

@inproceedings{6d955ece16af4ea08ab943f541b81571,
title = "Adapting information retrieval to user signals via stochastic models",
abstract = "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.",
keywords = "Evaluation, Markov precision, User model",
author = "Maria Maistro",
year = "2017",
month = feb,
day = "2",
doi = "10.1145/3018661.3022753",
language = "English",
booktitle = "WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining",
publisher = "Association for Computing Machinery, Inc.",
note = "10th ACM International Conference on Web Search and Data Mining, WSDM 2017 ; Conference date: 06-02-2017 Through 10-02-2017",

}

RIS

TY - GEN

T1 - Adapting information retrieval to user signals via stochastic models

AU - Maistro, Maria

PY - 2017/2/2

Y1 - 2017/2/2

N2 - 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.

AB - 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.

KW - Evaluation

KW - Markov precision

KW - User model

UR - http://www.scopus.com/inward/record.url?scp=85015345374&partnerID=8YFLogxK

U2 - 10.1145/3018661.3022753

DO - 10.1145/3018661.3022753

M3 - Article in proceedings

AN - SCOPUS:85015345374

BT - WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining

PB - Association for Computing Machinery, Inc.

T2 - 10th ACM International Conference on Web Search and Data Mining, WSDM 2017

Y2 - 6 February 2017 through 10 February 2017

ER -

ID: 216517423