Towards query log based personalization using topic models

Mark J. Carman; Fabio Crestani; Morgan Harvey; Mark Baillie

doi:10.1145/1871437.1871745

Towards query log based personalization using topic models

Mark J. Carman, Fabio Crestani, Morgan Harvey, Mark Baillie

Computer Information Sciences Department

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

71 Citations (Scopus)

Abstract

We investigate the utility of topic models for the task of personalizing search results based on information present in a large query log. We define generative models that take both the user and the clicked document into account when estimating the probability of query terms. These models can then be used to rank documents by their likelihood given a particular query and user pair.

Original language	English
Title of host publication	Proceedings of the 19th ACM International Conference on Information and Knowledge Management
Place of Publication	New York
Publisher	ACM
Pages	1849-1852
ISBN (Print)	978-1-4503-0099-5
DOIs	https://doi.org/10.1145/1871437.1871745
Publication status	Published - 2010
Event	Proceedings of the 19th {ACM} Conference on Information and Knowledge Management, {CIKM} 2010, Toronto, Ontario, Canada, October 26-30, 2010 - Duration: 1 Jan 2010 → …

Conference

Conference	Proceedings of the 19th {ACM} Conference on Information and Knowledge Management, {CIKM} 2010, Toronto, Ontario, Canada, October 26-30, 2010
Period	1/01/10 → …

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10.1145/1871437.1871745

Cite this

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title = "Towards query log based personalization using topic models",

abstract = "We investigate the utility of topic models for the task of personalizing search results based on information present in a large query log. We define generative models that take both the user and the clicked document into account when estimating the probability of query terms. These models can then be used to rank documents by their likelihood given a particular query and user pair.",

author = "Carman, {Mark J.} and Fabio Crestani and Morgan Harvey and Mark Baillie",

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note = "Proceedings of the 19th {ACM} Conference on Information and Knowledge Management, {CIKM} 2010, Toronto, Ontario, Canada, October 26-30, 2010 ; Conference date: 01-01-2010",

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Carman, MJ, Crestani, F, Harvey, M & Baillie, M 2010, Towards query log based personalization using topic models. in Proceedings of the 19th ACM International Conference on Information and Knowledge Management. ACM, New York, pp. 1849-1852, Proceedings of the 19th {ACM} Conference on Information and Knowledge Management, {CIKM} 2010, Toronto, Ontario, Canada, October 26-30, 2010, 1/01/10. https://doi.org/10.1145/1871437.1871745

TY - CHAP

T1 - Towards query log based personalization using topic models

AU - Carman, Mark J.

AU - Crestani, Fabio

AU - Harvey, Morgan

AU - Baillie, Mark

PY - 2010

Y1 - 2010

N2 - We investigate the utility of topic models for the task of personalizing search results based on information present in a large query log. We define generative models that take both the user and the clicked document into account when estimating the probability of query terms. These models can then be used to rank documents by their likelihood given a particular query and user pair.

AB - We investigate the utility of topic models for the task of personalizing search results based on information present in a large query log. We define generative models that take both the user and the clicked document into account when estimating the probability of query terms. These models can then be used to rank documents by their likelihood given a particular query and user pair.

U2 - 10.1145/1871437.1871745

DO - 10.1145/1871437.1871745

M3 - Chapter

SN - 978-1-4503-0099-5

SP - 1849

EP - 1852

BT - Proceedings of the 19th ACM International Conference on Information and Knowledge Management

PB - ACM

CY - New York

T2 - Proceedings of the 19th {ACM} Conference on Information and Knowledge Management, {CIKM} 2010, Toronto, Ontario, Canada, October 26-30, 2010

Y2 - 1 January 2010

ER -

Towards query log based personalization using topic models

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