Learning from User Interactions

Abstract

The ability to learn from user interactions can give systems access to unprecedented amounts of world knowledge. This is already evident in search engines, recommender systems, and electronic commerce, and other applications are likely to follow in the near future (e.g., education, smart homes). More generally, the ability to learn from user interactions promises pathways for solving knowledge-intensive tasks ranging from natural language understanding to autonomous robotics. Learning from user interactions, however, means learning from data that does not necessarily fit the assumptions of the standard machine learning models. Since interaction data consists of the choices that humans make, it has to be interpreted with respect to how humans make decisions, which is influenced by the decision context and constraints like human motivation and human abilities.