Intelligent tagging systems: machine learning for novel interaction

The Web puts a vast repository of information at users' fingertips, but the size and complexity of this information space can easily overwhelm users. Recommender systems and tagging systems represent two very different approaches to addressing this information overload. Recommender systems use machine learning and statistical models to automatically retrieve the items of most interest to a particular user. Tagging systems leverage the community's collective knowledge to help users explore the information space themselves. While both approaches can be very effective, they each have limitations. Recommender systems require little effort from users, but they leave users with little control over the recommendation process. Tagging systems put control in the hands of the user, but—because tags are applied by humans—tagging systems often suffer from issues of tag sparsity. This thesis explores intelligent tagging systems that combine the machine intelligence of recommender systems with the user control and comprehensibility of tagging systems. We first present Tagsplanations, tag-based explanations that help users understand why an item was recommended to them. We then introduce the Tag Genome, a novel data structure that uses machine learning to augment human judgments of the relationships between tags and items. Next we discuss Movie Tuner, a conversational recommender system based on the Tag Genome that enables users to provide multifaceted feedback using tags. For each system, we outline the design space of the problem and discuss our design decisions. We evaluate each system using both offline analyses as well as field studies involving thousands of users from MovieLens, a movie recommender system that also supports tagging of movies. Finally, we draw conclusions for the broader space of related applications.