Internet users are increasingly inclined to contribute comments to online news articles, videos, product reviews, and blogs. The most common interface for comments is a list, sorted by time of entry or by binary ratings. It is widely recognized that such lists do not scale well and can lead to "cyberpolarization," which serves to reinforce extreme opinions. We present Opinion Space: a new online interface incorporating ideas from deliberative polling, dimensionality reduction, and collaborative filtering that allows participants to visualize and navigate through a diversity of comments. This self-organizing system automatically highlights the comments found most insightful by users from a range of perspectives. We report results of a controlled user study. When Opinion Space was compared with a chronological List interface, participants read a similar diversity of comments. However, they were significantly more engaged with the system, and they had significantly higher agreement with and respect for the comments they read.

This extended abstract presents a new spatial model for collaboratively recommending compelling comments in an online discussion forum that promote consensus among a diverse group of users. In this application our goal is to promote comments that are rated highly by dissimilar users, which in some sense is a dual to traditional recommender problems. We propose a model for weighting and aggregating comment ratings that gives greater influence to positive ratings from users who tend to disagree with the commenter, and we compare it with various alternate methods. The model has the added benefit of being resistant to manipulation by false ratings and sybil attacks.We test the model on comments in Opinion Space, a new online discussion tool that allows users to visualize where they stand with respect to other users in terms of their opinions on a set of controversial propositions. Comments in the system are recommended visually, where more compelling comments are emphasized with larger sizes.

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.

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.