A large-scale analysis of query logs for assessing personalization opportunities
S. Wedig, и O. Madani. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, стр. 742--747. New York, NY, USA, ACM, (2006)
DOI: 10.1145/1150402.1150497
Аннотация
Query logs, the patterns of activity left by millions of users, contain a wealth of information that can be mined to aid personalization. We perform a large-scale study of Yahoo! search engine logs, tracking 1.35 million browser-cookies over a period of 6 months. We define metrics to address questions such as 1) How much history is available?, 2) How do users' topical interests vary, as reflected by their queries?, and 3) What can we learn from user clicks? We find that there is significantly more expected history for the user of a randomly picked query than for a randomly picked user. We show that users exhibit consistent topical interests that vary between users. We also see that user clicks indicate a variety of special interests. Our findings shed light on user activity and can inform future personalization efforts.
Описание
A large-scale analysis of query logs for assessing personalization opportunities
%0 Conference Paper
%1 wedig2006largescale
%A Wedig, Steve
%A Madani, Omid
%B Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
%C New York, NY, USA
%D 2006
%I ACM
%K implicit-feedback personalization search social-search
%P 742--747
%R 10.1145/1150402.1150497
%T A large-scale analysis of query logs for assessing personalization opportunities
%U http://doi.acm.org/10.1145/1150402.1150497
%X Query logs, the patterns of activity left by millions of users, contain a wealth of information that can be mined to aid personalization. We perform a large-scale study of Yahoo! search engine logs, tracking 1.35 million browser-cookies over a period of 6 months. We define metrics to address questions such as 1) How much history is available?, 2) How do users' topical interests vary, as reflected by their queries?, and 3) What can we learn from user clicks? We find that there is significantly more expected history for the user of a randomly picked query than for a randomly picked user. We show that users exhibit consistent topical interests that vary between users. We also see that user clicks indicate a variety of special interests. Our findings shed light on user activity and can inform future personalization efforts.
%@ 1-59593-339-5
@inproceedings{wedig2006largescale,
abstract = {Query logs, the patterns of activity left by millions of users, contain a wealth of information that can be mined to aid personalization. We perform a large-scale study of Yahoo! search engine logs, tracking 1.35 million browser-cookies over a period of 6 months. We define metrics to address questions such as 1) How much history is available?, 2) How do users' topical interests vary, as reflected by their queries?, and 3) What can we learn from user clicks? We find that there is significantly more expected history for the user of a randomly picked query than for a randomly picked user. We show that users exhibit consistent topical interests that vary between users. We also see that user clicks indicate a variety of special interests. Our findings shed light on user activity and can inform future personalization efforts.},
acmid = {1150497},
added-at = {2011-07-21T16:43:19.000+0200},
address = {New York, NY, USA},
author = {Wedig, Steve and Madani, Omid},
biburl = {https://www.bibsonomy.org/bibtex/203b49dce09a7aff8499925a669368da2/beate},
booktitle = {Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining},
description = {A large-scale analysis of query logs for assessing personalization opportunities},
doi = {10.1145/1150402.1150497},
interhash = {efee72edeaa86b6d81c0497102de7242},
intrahash = {03b49dce09a7aff8499925a669368da2},
isbn = {1-59593-339-5},
keywords = {implicit-feedback personalization search social-search},
location = {Philadelphia, PA, USA},
numpages = {6},
pages = {742--747},
publisher = {ACM},
series = {KDD '06},
timestamp = {2011-07-21T16:43:19.000+0200},
title = {A large-scale analysis of query logs for assessing personalization opportunities},
url = {http://doi.acm.org/10.1145/1150402.1150497},
year = 2006
}