# Recommender Systems with Heterogeneous Side Information

arXiv: https://arxiv.org/abs/1907.08679 · HTML: https://mlsanity.com/paper/1907.08679/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://mlsanity.com/paper/2204.05125/) (2022)
- [A Directional Diffusion Graph Transformer for Recommendation](https://mlsanity.com/paper/2404.03326/) (2024)
- [MTmixAtt: Integrating Mixture-of-Experts with Multi-Mix Attention for Large-Scale Recommendation](https://mlsanity.com/paper/2510.15286/) (2025)
- [A Design of A Simple Yet Effective Exercise Recommendation System in K-12 Online Learning](https://mlsanity.com/paper/2206.12291/) (2022)
- [Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering](https://mlsanity.com/paper/2203.07463/) (2022)
- [Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints](https://mlsanity.com/paper/2206.00707/) (2022)
- [Attention-based sequential recommendation system using multimodal data](https://mlsanity.com/paper/2405.17959/) (2024)
- [Direct side information learning for zero-shot regression](https://mlsanity.com/paper/2402.01264/) (2024)
- [Review-Based Hyperbolic Cross-Domain Recommendation](https://mlsanity.com/paper/2403.20298/) (2024)
- [General Item Representation Learning for Cold-start Content Recommendations](https://mlsanity.com/paper/2404.13808/) (2024)
- [Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study](https://mlsanity.com/paper/2607.27577/) (2026)

## Abstract (arXiv)

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.