Using Taste Groups for Collaborative Filtering

TL;DR

This paper introduces a novel collaborative filtering method that leverages taste groups identified through Hierarchical Latent Tree Analysis to improve recommendations from implicit feedback data, especially addressing the lack of negative examples.

Contribution

It proposes using taste groups to infer item relevance, providing a new approach to handle implicit feedback without relying on negative examples.

Findings

Effective identification of taste groups using HLTA.

Improved recommendation accuracy in implicit feedback scenarios.

Addresses the negative example problem in collaborative filtering.

Abstract

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.