Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
C4.5: programs for machine learning
C4.5: programs for machine learning
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning in the presence of concept drift and hidden contexts
Machine Learning
Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The Complexity of Learning According to Two Models of a Drifting Environment
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Specifying preferences based on user history
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Changing Concepts by Exploiting the Structure of Change
Machine Learning
DEMON: Mining and Monitoring Evolving Data
IEEE Transactions on Knowledge and Data Engineering
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
Collaborative Filtering Using Weighted Majority Prediction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
On the Temporal Analysis for Improved Hybrid Recommendations
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
The Hybrid Poisson Aspect Model for Personalized Shopping Recommendation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient decision tree construction on streaming data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Information Systems (TOIS)
A comparison of several predictive algorithms for collaborative filtering on multi-valued ratings
Proceedings of the 2004 ACM symposium on Applied computing
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
A joint framework for collaborative and content filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A recent-biased dimension reduction technique for time series data
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Ambiguous decision trees for mining concept-drifting data streams
Pattern Recognition Letters
A recommender system with interest-drifting
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
Unified collaborative filtering model based on combination of latent features
Expert Systems with Applications: An International Journal
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Collaborate filtering is one of the most popular recommendation algorithms. Most collaborative filtering algorithms work with static data. This paper introduces a novel approach to providing recommendations using collaborative filtering when user rating is arrived over an incoming data stream. In this case a large number of data records can arrive rapidly making it impossible to save all of them for later analysis. Moreover, user interests may change over time. By dynamically building a decision tree for every item as data arrive, the incoming data stream is used effectively with a trade off between catching up the changes of users interests and accuracy. By adding a simple step using a hierarchy of items taxonomy, it is also possible to further improve the predicted ratings made by each decision tree and generate recommendations in realtime. Empirical studies with the dynamically built decision trees show that our algorithm works effectively and improves the overall prediction accuracy.