We study the problem of learning personalized user models from rich user interactions. In particular, we focus on learning from clustering feedback (i.e., grouping recommended items into clusters), which enables users to express similarity or redundancy between different items. We propose and study a new machine learning problem for personalization, which we call collaborative clustering. Analogous to collaborative filtering, in collaborative clustering the goal is to leverage how existing users cluster or group items in order to predict similarity models for other users' clustering tasks. We propose a simple yet effective latent factor model to learn the variability of similarity functions across a user population. We empirically evaluate our approach using data collected from a clustering interface we developed for a goal-oriented data exploration (or sensemaking) task: asking users to explore and organize attractions in Paris. We evaluate using several realistic use cases, and show that our approach learns more effective user models than conventional clustering and metric learning approaches.

From Twitter to Facebook to Reddit, users have become accustomed to sharing the articles they read with friends or followers on their social networks. While previous work has modeled what these shared stories say about the user who shares them, the converse question remains unexplored: what can we learn about an article from the identities of its likely readers? To address this question, we model the content of news articles and blog posts by attributes of the people who are likely to share them. For example, many Twitter users describe themselves in a short profile, labeling themselves with phrases such as "vegetarian" or "liberal." By assuming that a user's labels correspond to topics in the articles he shares, we can learn a labeled dictionary from a training corpus of articles shared on Twitter. Thereafter, we can code any new document as a sparse non-negative linear combination of user labels, where we encourage correlated labels to appear together in the output via a structured sparsity penalty.

Personalization is a ubiquitous phenomenon in our daily online experience. While such technology is critical for helping us combat the overload of information we face, in many cases, we may not even realize that our results are being tailored to our personal tastes and preferences. Worse yet, when such a system makes a mistake, we have little recourse to correct it.

In scientific research, it is often difficult to express information needs as simple keyword queries. We present a more natural way of searching for relevant scientific literature. Rather than a string of keywords, we define a query as a small set of papers deemed relevant to the research task at hand. By optimizing an objective function based on a fine-grained notion of influence between documents, our approach efficiently selects a set of highly relevant articles. Moreover, as scientists trust some authors more than others, results are personalized to individual preferences. In a user study, researchers found the papers recommended by our method to be more useful, trustworthy and diverse than those selected by popular alternatives, such as Google Scholar and a state-of-the-art topic modeling approach.

In recent years, the blogosphere has experienced a substantial increase in the number of posts published daily, forcing users to cope with information overload. The task of guiding users through this flood of information has thus become critical. To address this issue, we present a principled approach for picking a set of posts that best covers the important stories in the blogosphere.