# Recommender Systems with Heterogeneous Side Information

## TL;DR

This paper introduces a novel framework for recommender systems that effectively leverages both flat and hierarchical heterogeneous side information, leading to significant performance improvements.

## Contribution

The paper proposes a new framework that jointly models flat and hierarchical side information in recommender systems, addressing heterogeneity challenges.

## Key findings

- Significant performance gain over state-of-the-art methods
- Effective integration of heterogeneous side information
- Validated on multiple real-world datasets

## Abstract

In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items. Such information is typically heterogeneous and can be roughly categorized into flat and hierarchical side information. While side information has been proved to be valuable, the majority of existing systems have exploited either only flat side information or only hierarchical side information due to the challenges brought by the heterogeneity. In this paper, we investigate the problem of exploiting heterogeneous side information for recommendations. Specifically, we propose a novel framework jointly captures flat and hierarchical side information with mathematical coherence. We demonstrate the effectiveness of the proposed framework via extensive experiments on various real-world datasets. Empirical results show that our approach is able to lead a significant performance gain over the state-of-the-art methods.