Learning Item Trees for Probabilistic Modelling of Implicit Feedback

TL;DR

This paper presents a probabilistic model for collaborative filtering using implicit feedback, employing item trees for scalability, and proposes an improved evaluation protocol incorporating explicit feedback.

Contribution

It introduces a novel probabilistic approach with item trees for implicit feedback and addresses evaluation issues with a new protocol using explicit feedback.

Findings

Efficient algorithm for learning item trees from data.

Improved evaluation protocol for implicit feedback models.

Scalable probabilistic model for implicit feedback.

Abstract

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.