In most previous work on personalized search algorithms, the
results for all queries are personalized in the same manner.
However, as we show in this paper, there is a lot of variation
across queries in the benefits that can be achieved through
personalization. For some queries, everyone who issues the query
is looking for the same thing. For other queries, different people
want very different results even though they express their need in
the same way. We examine variability in user intent using both
explicit relevance judgments and large-scale log analysis of user
behavior patterns. While variation in user behavior is correlated
with variation in explicit relevance judgments the same query,
there are many other factors, such as result entropy, result quality,
and task that can also affect the variation in behavior. We
characterize queries using a variety of features of the query, the
results returned for the query, and people's interaction history with
the query. Using these features we build predictive models to
identify queries that can benefit from personalization.
%0 Conference Paper
%1 paper:teevan:2008
%A Teevan, Jaime
%A Dumais, Susan T.
%A Liebling, Daniel J.
%B SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
%C New York, NY, USA
%D 2008
%I ACM
%K 2008 IR intent lab-meeting query
%P 163--170
%R http://doi.acm.org/10.1145/1390334.1390364
%T To personalize or not to personalize: modeling queries with variation in user intent
%U http://portal.acm.org/citation.cfm?doid=1390334.1390364
%X In most previous work on personalized search algorithms, the
results for all queries are personalized in the same manner.
However, as we show in this paper, there is a lot of variation
across queries in the benefits that can be achieved through
personalization. For some queries, everyone who issues the query
is looking for the same thing. For other queries, different people
want very different results even though they express their need in
the same way. We examine variability in user intent using both
explicit relevance judgments and large-scale log analysis of user
behavior patterns. While variation in user behavior is correlated
with variation in explicit relevance judgments the same query,
there are many other factors, such as result entropy, result quality,
and task that can also affect the variation in behavior. We
characterize queries using a variety of features of the query, the
results returned for the query, and people's interaction history with
the query. Using these features we build predictive models to
identify queries that can benefit from personalization.
%@ 978-1-60558-164-4
@inproceedings{paper:teevan:2008,
abstract = {In most previous work on personalized search algorithms, the
results for all queries are personalized in the same manner.
However, as we show in this paper, there is a lot of variation
across queries in the benefits that can be achieved through
personalization. For some queries, everyone who issues the query
is looking for the same thing. For other queries, different people
want very different results even though they express their need in
the same way. We examine variability in user intent using both
explicit relevance judgments and large-scale log analysis of user
behavior patterns. While variation in user behavior is correlated
with variation in explicit relevance judgments the same query,
there are many other factors, such as result entropy, result quality,
and task that can also affect the variation in behavior. We
characterize queries using a variety of features of the query, the
results returned for the query, and people's interaction history with
the query. Using these features we build predictive models to
identify queries that can benefit from personalization.},
added-at = {2008-09-22T11:03:49.000+0200},
address = {New York, NY, USA},
author = {Teevan, Jaime and Dumais, Susan T. and Liebling, Daniel J.},
biburl = {https://www.bibsonomy.org/bibtex/2dbcf005ffc1f5e4826b0d5f140d4c853/mschuber},
booktitle = {SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval},
description = {To personalize or not to personalize},
doi = {http://doi.acm.org/10.1145/1390334.1390364},
interhash = {7321916fcf1ccc30ee85d3fa66d4fb46},
intrahash = {dbcf005ffc1f5e4826b0d5f140d4c853},
isbn = {978-1-60558-164-4},
keywords = {2008 IR intent lab-meeting query},
location = {Singapore, Singapore},
pages = {163--170},
publisher = {ACM},
timestamp = {2008-09-24T13:12:58.000+0200},
title = {To personalize or not to personalize: modeling queries with variation in user intent},
url = {http://portal.acm.org/citation.cfm?doid=1390334.1390364},
year = 2008
}