  User preferences for items can be inferred from either explicit feedback,
such as item ratings, or implicit feedback, such as rental histories. Research
in collaborative filtering has concentrated on explicit feedback, resulting in
the development of accurate and scalable models. However, since explicit
feedback is often difficult to collect it is important to develop effective
models that take advantage of the more widely available implicit feedback. We
introduce a probabilistic approach to collaborative filtering with implicit
feedback based on modelling the user's item selection process. In the interests
of scalability, we restrict our attention to tree-structured distributions over
items and develop a principled and efficient algorithm for learning item trees
from data. We also identify a problem with a widely used protocol for
evaluating implicit feedback models and propose a way of addressing it using a
small quantity of explicit feedback data.
