Learning routing queries in a query zone
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A learning agent for wireless news access
Proceedings of the 5th international conference on Intelligent user interfaces
Unsupervised and supervised clustering for topic tracking
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Detection As Multi-Topic Tracking
Information Retrieval
Customized Internet news services based on customer profiles
ICEC '03 Proceedings of the 5th international conference on Electronic commerce
Evaluating adaptive user profiles for news classification
Proceedings of the 9th international conference on Intelligent user interfaces
Simple Semantics in Topic Detection and Tracking
Information Retrieval
WWW '05 Proceedings of the 14th international conference on World Wide Web
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Finding near-duplicate web pages: a large-scale evaluation of algorithms
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent query analysis for combining multiple retrieval sources
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Maintaining dynamic channel profiles on the web
Proceedings of the VLDB Endowment
Online selection of parameters in the rocchio algorithm for identifying interesting news articles
Proceedings of the 10th ACM workshop on Web information and data management
Constrained Local Regularized Transducer for Multi-Component Category Classification
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
A new framework for analyzing political news
Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government
Document recommendation in social tagging services
Proceedings of the 19th international conference on World wide web
Mining positive and negative patterns for relevance feature discovery
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning a user-thread alignment manifold for thread recommendation in online forum
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Measuring the interestingness of articles in a limited user environment
Information Processing and Management: an International Journal
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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% 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.