  Internet based businesses and products (e.g. e-commerce, music streaming) are
becoming more and more sophisticated every day with a lot of focus on improving
customer satisfaction. A core way they achieve this is by providing customers
with an easy access to their products by structuring them in catalogues using
navigation bars and providing recommendations. We refer to these catalogues as
product concepts, e.g. product categories on e-commerce websites, public
playlists on music streaming platforms. These product concepts typically
contain products that are linked with each other through some common features
(e.g. a playlist of songs by the same artist). How they are defined in the
backend of the system can be different for different products. In this work, we
represent product concepts using database queries and tackle two learning
problems. First, given sets of products that all belong to the same unknown
product concept, we learn a database query that is a representation of this
product concept. Second, we learn product concepts and their corresponding
queries when the given sets of products are associated with multiple product
concepts. To achieve these goals, we propose two approaches that combine the
concepts of PU learning with Decision Trees and Clustering. Our experiments
demonstrate, via a simulated setup for a music streaming service, that our
approach is effective in solving these problems.
