  We present a collection recommender system that can automatically create and
recommend collections of items at a user level. Unlike regular recommender
systems, which output top-N relevant items, a collection recommender system
outputs collections of items such that the items in the collections are
relevant to a user, and the items within a collection follow a specific theme.
Our system builds on top of the user-item representations learnt by item
recommender systems. We employ dimensionality reduction and clustering
techniques along with intuitive heuristics to create collections with their
ratings and titles.
  We test these ideas in a real-world setting of music recommendation, within a
popular music streaming service. We find that there is a 2.3x increase in
recommendation-driven consumption when recommending collections over items.
Further, it results in effective utilization of real estate and leads to
recommending a more and diverse set of items. To our knowledge, these are first
of its kind experiments at such a large scale.
