# DGCN: Diversified Recommendation with Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2108.06952 · HTML: https://mlsanity.com/paper/2108.06952/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2021.

## Built on by (most cited first, 29 of 29)

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- [PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization](https://mlsanity.com/paper/2603.19649/) (2026)
- [Improved Diversity-Promoting Collaborative Metric Learning for Recommendation](https://mlsanity.com/paper/2409.01012/) (2024)
- [KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction](https://mlsanity.com/paper/2302.12126/) (2023)
- [Heterophily-Aware Fair Recommendation using Graph Convolutional Networks](https://mlsanity.com/paper/2402.03365/) (2024)
- [A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions](https://mlsanity.com/paper/2412.12836/) (2024)
- [GRASP: GRAph-Structured Pyramidal Whole Slide Image Representation](https://mlsanity.com/paper/2402.03592/) (2024)
- [Disentangled Representation for Diversified Recommendations](https://mlsanity.com/paper/2301.05492/) (2023)
- [RAGSys: Item-Cold-Start Recommender as RAG System](https://mlsanity.com/paper/2405.17587/) (2024)
- [ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender Systems](https://mlsanity.com/paper/2407.13163/) (2024)
- [Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization](https://mlsanity.com/paper/2502.13180/) (2025)
- [Dual-Phase Playtime-guided Recommendation: Interest Intensity Exploration and Multimodal Random Walks](https://mlsanity.com/paper/2508.14058/) (2025)
- [Learning from one graph: transductive learning guarantees via the geometry of small random worlds](https://mlsanity.com/paper/2509.06894/) (2025)
- [CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations](https://mlsanity.com/paper/2604.14586/) (2026)
- [Denoising Long- and Short-term Interests for Sequential Recommendation](https://mlsanity.com/paper/2407.14743/) (2024)
- [Quantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation](https://mlsanity.com/paper/2512.03067/) (2025)
- [Tree of Preferences for Diversified Recommendation](https://mlsanity.com/paper/2601.02386/) (2025)
- [Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2306.10456/) (2023)
- [Topic-Level Bayesian Surprise and Serendipity for Recommender Systems](https://mlsanity.com/paper/2308.06368/) (2023)
- [Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation](https://mlsanity.com/paper/2403.04511/) (2024)
- [Simulating Filter Bubble on Short-video Recommender System with Large Language Model Agents](https://mlsanity.com/paper/2504.08742/) (2025)
- [Contrastive Adaptive Propagation Graph Neural Networks for Efficient Graph Learning](https://mlsanity.com/paper/2112.01110/) (2021)
- [Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender](https://mlsanity.com/paper/2306.00009/) (2023)
- [A Simple Yet Effective Approach for Diversified Session-Based Recommendation](https://mlsanity.com/paper/2404.00261/) (2024)
- [Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure](https://mlsanity.com/paper/2512.17733/) (2025)
- [Disentangled Interest Network for Out-of-Distribution CTR Prediction](https://mlsanity.com/paper/2602.00002/) (2025)

## Abstract (arXiv)

These years much effort has been devoted to improving the accuracy or relevance of the recommendation system. Diversity, a crucial factor which measures the dissimilarity among the recommended items, received rather little scrutiny. Directly related to user satisfaction, diversification is usually taken into consideration after generating the candidate items. However, this decoupled design of diversification and candidate generation makes the whole system suboptimal. In this paper, we aim at pushing the diversification to the upstream candidate generation stage, with the help of Graph Convolutional Networks (GCN). Although GCN based recommendation algorithms have shown great power in modeling complex collaborative filtering effect to improve the accuracy of recommendation, how diversity changes is ignored in those advanced works. We propose to perform rebalanced neighbor discovering, category-boosted negative sampling and adversarial learning on top of GCN. We conduct extensive experiments on real-world datasets. Experimental results verify the effectiveness of our proposed method on diversification. Further ablation studies validate that our proposed method significantly alleviates the accuracy-diversity dilemma.