Difference between revisions of "Liu K and Tang L. CIKM'11"
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This paper reports experiments on assessing the effectiveness of supervised and unsupervised learning methods in leveraging social data in [http://en.wikipedia.org/wiki/Behavioral_targeting behavioral targeting] problem. The experiments were conducted on a large scale dataset that contains information about users' activities and their social networks from ``a large IT company" (mostly Yahoo) in 14 weeks. User activities include users' web-browsing behavior such as the page visiting, ad clicking, searching, etc. User network is built based on instant messages exchanged among users. The resulting dataset includes of 180 million users and their behaviors in 60 consumer domains. To deal with the large scale of the dataset, all the experiments are implemented in [http://en.wikipedia.org/wiki/MapReduce Hadoop MapReduce] framework. | This paper reports experiments on assessing the effectiveness of supervised and unsupervised learning methods in leveraging social data in [http://en.wikipedia.org/wiki/Behavioral_targeting behavioral targeting] problem. The experiments were conducted on a large scale dataset that contains information about users' activities and their social networks from ``a large IT company" (mostly Yahoo) in 14 weeks. User activities include users' web-browsing behavior such as the page visiting, ad clicking, searching, etc. User network is built based on instant messages exchanged among users. The resulting dataset includes of 180 million users and their behaviors in 60 consumer domains. To deal with the large scale of the dataset, all the experiments are implemented in [http://en.wikipedia.org/wiki/MapReduce Hadoop MapReduce] framework. | ||
| − | + | The problem is formulated as a prediction task: given users'activities and their network first 10 weeks, predict users' activities in last 4 weeks. Metric to assess the performance of different learning methods are the area under [http://en.wikipedia.org/wiki/Receiver_operating_characteristic ROC] and | |
Revision as of 22:02, 26 September 2012
Citation
@inproceedings{Liu:2011:LBT:2063576.2063838,
author = {Liu, Kun and Tang, Lei},
title = {Large-scale behavioral targeting with a social twist},
booktitle = {Proceedings of the 20th ACM international conference on Information and knowledge management},
series = {CIKM '11},
year = {2011},
isbn = {978-1-4503-0717-8},
location = {Glasgow, Scotland, UK},
pages = {1815--1824},
numpages = {10},
url = {http://doi.acm.org/10.1145/2063576.2063838},
doi = {10.1145/2063576.2063838},
acmid = {2063838},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {advertising, behavioral targeting, large-scale data mining, social targeting, social-network analysis}
}
Online Version
Large-Scale Behavioral Targeting with a Social Twist.
Summary
This paper reports experiments on assessing the effectiveness of supervised and unsupervised learning methods in leveraging social data in behavioral targeting problem. The experiments were conducted on a large scale dataset that contains information about users' activities and their social networks from ``a large IT company" (mostly Yahoo) in 14 weeks. User activities include users' web-browsing behavior such as the page visiting, ad clicking, searching, etc. User network is built based on instant messages exchanged among users. The resulting dataset includes of 180 million users and their behaviors in 60 consumer domains. To deal with the large scale of the dataset, all the experiments are implemented in Hadoop MapReduce framework.
The problem is formulated as a prediction task: given users'activities and their network first 10 weeks, predict users' activities in last 4 weeks. Metric to assess the performance of different learning methods are the area under ROC and
