Learning User's Preferred Household Organization via Collaborative Filtering Methods

As learning robots and smart devices become common household occurrences, their users will be required to invest more time to train them on the details specific to their household and lifestyle. This burden of personalization may eventually become a roadblock to the adoption of smart devices and robots. We are interested in reducing the burden of personalization by leveraging learned information from other households. However, machine learning methods incorporating such data will require smart recontextualizations that can map the preferences from a collection of similar users onto the user’s own household space. We present several collaborative filtering based methods to solve the problem of a robot organizing the items in a kitchen for their user: a traditional collaborative filtering method based on prior work incorporating user’s item–item distance ratings, and a context-aware collaborative filtering method, which enables direct learning of item–location ratings. We present results on user-annotated kitchens.