# Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure

arXiv: https://arxiv.org/abs/2512.17733 · HTML: https://mlsanity.com/paper/2512.17733/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 19 Dec 2025.

## Builds on (most cited first, 22 of 22)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Counterfactual Explainable Recommendation](https://mlsanity.com/paper/2108.10539/) (2021)
- [CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation](https://mlsanity.com/paper/2109.05261/) (2021)
- [DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation](https://mlsanity.com/paper/2211.10486/) (2022)
- [DGCN: Diversified Recommendation with Graph Convolutional Networks](https://mlsanity.com/paper/2108.06952/) (2021)
- [User-controllable Recommendation Against Filter Bubbles](https://mlsanity.com/paper/2204.13844/) (2022)
- [Double Correction Framework for Denoising Recommendation](https://mlsanity.com/paper/2405.11272/) (2024)
- [Sliding Spectrum Decomposition for Diversified Recommendation](https://mlsanity.com/paper/2107.05204/) (2021)
- [RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation](https://mlsanity.com/paper/2205.14005/) (2022)
- [AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations](https://mlsanity.com/paper/2403.17416/) (2024)
- [Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation](https://mlsanity.com/paper/2206.05020/) (2022)
- [Recommending Accurate and Diverse Items Using Bilateral Branch Network](https://mlsanity.com/paper/2101.00781/) (2021)
- [Improving Conversational Recommendation Systems via Counterfactual Data Simulation](https://mlsanity.com/paper/2306.02842/) (2023)
- [Reconciling the accuracy-diversity trade-off in recommendations](https://mlsanity.com/paper/2307.15142/) (2023)
- [Causal Disentangled Recommendation Against User Preference Shifts](https://mlsanity.com/paper/2303.16068/) (2023)
- [Path-Specific Counterfactual Fairness for Recommender Systems](https://mlsanity.com/paper/2306.02615/) (2023)
- [Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation](https://mlsanity.com/paper/2401.05744/) (2024)
- [Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation](https://mlsanity.com/paper/2207.05959/) (2022)
- [Dual-Phase Playtime-guided Recommendation: Interest Intensity Exploration and Multimodal Random Walks](https://mlsanity.com/paper/2508.14058/) (2025)
- [DRGame: Diversified Recommendation for Multi-category Video Games with Balanced Implicit Preferences](https://mlsanity.com/paper/2308.15823/) (2023)
- [Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive Next Basket Recommendation](https://mlsanity.com/paper/2409.11695/) (2024)
- [CDC: Causal Domain Clustering for Multi-Domain Recommendation](https://mlsanity.com/paper/2507.06877/) (2025)

## Abstract (arXiv)

Beyond user-item modeling, item-to-item relationships are increasingly used to enhance recommendation. However, common methods largely rely on co-occurrence, making them prone to item popularity bias and user attributes, which degrades embedding quality and performance. Meanwhile, although diversity is acknowledged as a key aspect of recommendation quality, existing research offers limited attention to it, with a notable lack of causal perspectives and theoretical grounding. To address these challenges, we propose Cadence: Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure - a plug-and-play framework built upon LightGCN as the backbone, primarily designed to enhance recommendation diversity while preserving accuracy. First, we compute the Unbiased Asymmetric Co-purchase Relationship (UACR) between items - excluding item popularity and user attributes - to construct a deconfounded directed item graph, with an aggregation mechanism to refine embeddings. Second, we leverage UACR to identify diverse categories of items that exhibit strong causal relevance to a user's interacted items but have not yet been engaged with. We then simulate their behavior under high-exposure scenarios, thereby significantly enhancing recommendation diversity while preserving relevance. Extensive experiments on real-world datasets demonstrate that our method consistently outperforms state-of-the-art diversity models in both diversity and accuracy, and further validates its effectiveness, transferability, and efficiency over baselines.