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Title: Tracking Multiple Topics for Finding Interesting Articles

Abstract

We introduce multiple topic tracking (MTT) for iScore to better recommend news articles for users with multiple interests and to address changes in user interests over time. As an extension of the basic Rocchio algorithm, traditional topic detection and tracking, and single-pass clustering, MTT maintains multiple interest profiles to identify interesting articles for a specific user given user-feedback. Focusing on only interesting topics enables iScore to discard useless profiles to address changes in user interests and to achieve a balance between resource consumption and classification accuracy. Also by relating a topic's interestingness to an article's interestingness, iScore is able to achieve higher quality results than traditional methods such as the Rocchio algorithm. We identify several operating parameters that work well for MTT. Using the same parameters, we show that MTT alone yields high quality results for recommending interesting articles from several corpora. The inclusion of MTT improves iScore's performance by 9% to 14% in recommending news articles from the Yahoo! News RSS feeds and the TREC11 adaptive filter article collection. And through a small user study, we show that iScore can still perform well when only provided with little user feedback.

Authors:
; ; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
913552
Report Number(s):
UCRL-CONF-228286
TRN: US200802%%871
DOE Contract Number:
W-7405-ENG-48
Resource Type:
Conference
Resource Relation:
Conference: Presented at: Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, United States, Aug 12 - Aug 15, 2007
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ACCURACY; CLASSIFICATION; DETECTION; FEEDBACK; FOCUSING; MINING; PERFORMANCE

Citation Formats

Pon, R K, Cardenas, A F, Buttler, D J, and Critchlow, T J. Tracking Multiple Topics for Finding Interesting Articles. United States: N. p., 2007. Web.
Pon, R K, Cardenas, A F, Buttler, D J, & Critchlow, T J. Tracking Multiple Topics for Finding Interesting Articles. United States.
Pon, R K, Cardenas, A F, Buttler, D J, and Critchlow, T J. Thu . "Tracking Multiple Topics for Finding Interesting Articles". United States. doi:. https://www.osti.gov/servlets/purl/913552.
@article{osti_913552,
title = {Tracking Multiple Topics for Finding Interesting Articles},
author = {Pon, R K and Cardenas, A F and Buttler, D J and Critchlow, T J},
abstractNote = {We introduce multiple topic tracking (MTT) for iScore to better recommend news articles for users with multiple interests and to address changes in user interests over time. As an extension of the basic Rocchio algorithm, traditional topic detection and tracking, and single-pass clustering, MTT maintains multiple interest profiles to identify interesting articles for a specific user given user-feedback. Focusing on only interesting topics enables iScore to discard useless profiles to address changes in user interests and to achieve a balance between resource consumption and classification accuracy. Also by relating a topic's interestingness to an article's interestingness, iScore is able to achieve higher quality results than traditional methods such as the Rocchio algorithm. We identify several operating parameters that work well for MTT. Using the same parameters, we show that MTT alone yields high quality results for recommending interesting articles from several corpora. The inclusion of MTT improves iScore's performance by 9% to 14% in recommending news articles from the Yahoo! News RSS feeds and the TREC11 adaptive filter article collection. And through a small user study, we show that iScore can still perform well when only provided with little user feedback.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Feb 15 00:00:00 EST 2007},
month = {Thu Feb 15 00:00:00 EST 2007}
}

Conference:
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