# Research Commentary on Recommendations with Side Information: A Survey and Research Directions

arXiv: https://arxiv.org/abs/1909.12807 · HTML: https://mlsanity.com/paper/1909.12807/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 23 of 23)

- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [A Survey of Knowledge Graph Embedding and Their Applications](https://mlsanity.com/paper/2107.07842/) (2021)
- [Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding](https://mlsanity.com/paper/2110.03789/) (2021)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation](https://mlsanity.com/paper/2403.16427/) (2024)
- [A Survey on Dropout Methods and Experimental Verification in Recommendation](https://mlsanity.com/paper/2204.02027/) (2022)
- [Augmenting the User-Item Graph with Textual Similarity Models](https://mlsanity.com/paper/2109.09358/) (2021)
- [Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items](https://mlsanity.com/paper/2201.13313/) (2022)
- [Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization](https://mlsanity.com/paper/2502.13180/) (2025)
- [Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation](https://mlsanity.com/paper/2304.07506/) (2023)
- [SemanticShield: LLM-Powered Audits Expose Shilling Attacks in Recommender Systems](https://mlsanity.com/paper/2509.24961/) (2025)
- [Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction](https://mlsanity.com/paper/2109.14766/) (2021)
- [Buy when? Survival machine learning model comparison for purchase timing](https://mlsanity.com/paper/2308.14343/) (2023)
- [Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization Collaborations](https://mlsanity.com/paper/2110.13340/) (2021)
- [Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation](https://mlsanity.com/paper/2207.14218/) (2022)
- [Recommender Systems: A Primer](https://mlsanity.com/paper/2302.02579/) (2023)
- [A Simple Yet Effective Approach for Diversified Session-Based Recommendation](https://mlsanity.com/paper/2404.00261/) (2024)
- [General Item Representation Learning for Cold-start Content Recommendations](https://mlsanity.com/paper/2404.13808/) (2024)
- [Guiding Catalogue Enrichment with User Queries](https://mlsanity.com/paper/2406.07098/) (2024)
- [Towards Fair and Rigorous Evaluations: Hyperparameter Optimization for Top-N Recommendation Task with Implicit Feedback](https://mlsanity.com/paper/2408.07630/) (2024)
- [Hierarchical Knowledge Graph Construction from Images for Scalable E-Commerce](https://mlsanity.com/paper/2410.21237/) (2024)
- [P3Rec: Distilling Prior--Posterior Preference Reasoning for LLM-based Recommendation](https://mlsanity.com/paper/2609.13993/) (2026)

## Abstract (arXiv)

Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning, and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.