Using Taste Groups for Collaborative Filtering
This addresses a key limitation in collaborative filtering for recommendation systems, though it is incremental as it builds on existing methods to handle implicit feedback.
The paper tackles the problem of lacking negative examples in implicit feedback for recommendation systems by proposing a method that identifies taste-based user groups using Hierarchical Latent Tree Analysis (HLTA) to infer item irrelevance, resulting in improved recommendations.
Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and domain independent. However, there is a lack of negative examples. Existing works circumvent this problem by making various assumptions regarding the unconsumed items, which fail to hold when the user did not consume an item because she was unaware of it. In this paper, we propose as a novel method for addressing the lack of negative examples in implicit feedback. The motivation is that if there is a large group of users who share the same taste and none of them consumed an item, then it is highly likely that the item is irrelevant to this taste. We use Hierarchical Latent Tree Analysis(HLTA) to identify taste-based user groups and make recommendations for a user based on her memberships in the groups.