Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
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
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Scale and Translation Invariant Collaborative Filtering Systems
Information Retrieval
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving Accuracy of Recommender System by Clustering Items Based on Stability of User Similarity
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Lessons on applying automated recommender systems to information-seeking tasks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Collaborative filtering based on iterative principal component analysis
Expert Systems with Applications: An International Journal
Web-Based Recommender Systems and User Needs --the Comprehensive View
Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems
Evaluating Interface Variants on Personality Acquisition for Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
A careful assessment of recommendation algorithms related to dimension reduction techniques
Knowledge-Based Systems
A recommendation model for handling dynamics in user profile
ICDCIT'12 Proceedings of the 8th international conference on Distributed Computing and Internet Technology
Generalized framework for personalized recommendations in agent networks
Autonomous Agents and Multi-Agent Systems
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Recommender systems strive to recommend items that users will appreciate and rate highly, often presenting items in order of highest predicted ratings first. In this working paper we present Eigentaste 5.0, a constant-time recommender system that dynamically adapts the order that items are recommended by integrating user clustering with item clustering and monitoring item portfolio effects. This extends our Eigentaste 2.0 algorithm, which uses principal component analysis to cluster users offline. In preliminary experiments we backtested Eigentaste 5.0 on data collected from Jester, our online joke recommender system. Results suggest that it will perform better than Eigentaste 2.0. The new algorithm also uses item clusters to address the cold-start problem for introducing new items.