Teachable Facets: A Framework of Interactive Machine Teaching for Information Filtering

Interactive tools help users filter relevant information from massive online sources, like news feeds and online discussion forums, by enabling them to externalize their preferences. However, users' information goals and preferences are often complex and are comprised of data attributes and a user's subjective judgements over these attributes. For instance, when filtering news articles based on their newsworthiness, the system must capture both data attributes like recency and shareability of the article, along with the user's personal and flexible assessment of news sentiment. While most interactive tools enable users to externalize goals that are expressible as true/false statements, they do not support incorporating subjective, loosely structured judgements of data attributes which fulfill complex goals. In this paper, we introduce Teachable Facets (TF), widgets that users can create on the fly to filter relevant information to improve the sense-making of analysts. These teachable widgets employ a Machine Teaching (MT) framework to enable users to formulate personalized filtering criteria for complex, multi-dimensional, loosely indexed, and unstructured data; teach a filtering criterion using representative samples; apply these filters to new data streams; and assess the relevance of outcomes. Through a user study, we evaluate the performance of these filters based on their ability to discover relevant items and the expressibility they offer to the users in teaching criteria. In our discussion, we identify ways this approach might improve future systems and delineate implications should such systems be deployed broadly.