Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
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Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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WWW '05 Proceedings of the 14th international conference on World Wide Web
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Complex-network theoretic clustering for identifying groups of similar listeners in p2p systems
Proceedings of the 2007 ACM conference on Recommender systems
Supporting product selection with query editing recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Conversational recommenders with adaptive suggestions
Proceedings of the 2007 ACM conference on Recommender systems
Incremental probabilistic latent semantic analysis for automatic question recommendation
Proceedings of the 2008 ACM conference on Recommender systems
Critique graphs for catalogue navigation
Proceedings of the 2008 ACM conference on Recommender systems
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
The information cost of manipulation-resistance in recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Unsupervised retrieval of attack profiles in collaborative recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
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In this paper we present Donation Dashboard, a system that recommends non-profit organizations to users in the form of a portfolio of donation amounts. Recommendations are made using our Eigentaste 2.0 constant-time collaborative filtering algorithm in combination with a new method for generating a weighted portfolio of recommendations. The key challenge is to generate a customized portfolio that does not necessarily exclude items already rated by the user. Under our method, the weights for items in the portfolio that have not yet been rated by the user are normalized factors of their predicted ratings, and the weights for items previously rated by the user are normalized factors of the actual ratings. Donation Dashboard 1.0 launched in April 2008, and as of May 8 2009 we have collected over 59,000 ratings of 70 nonprofit organizations from over 3,800 users. In this working paper we describe our experience developing Donation Dashboard, including the design of the system and our new method for portfolio generation. We use Normalized Mean Absolute Error (NMAE) to measure the accuracy of Eigentaste using our dataset of non-profit organization ratings and we compare that with the global mean algorithm. We analyze the data collected since the launch of the site, and we have made our dataset available to the public. Donation Dashboard and the Donation Dashboard dataset are accessible at: http://dd.berkeley.edu and http://dd.berkeley.edu/dataset