Learning Label Trees for Probabilistic Modelling of Implicit Feedback
Andriy Mnih, Yee Whye Teh · 2012
User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories. Research in collabora-tive filtering has concentrated on explicit feedback, resulting in the development of accurate and scalable models. However, since explicit feedback is often diffi-cult 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. 1