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

• URL: http://arxiv.org/abs/2512.17733v1
• Date: Fri, 19 Dec 2025 16:09:29 GMT
• Title: Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure
• Authors: Jingmao Zhang, Zhiting Zhao, Yunqi Lin, Jianghong Ma, Tianjun Wei, Haijun Zhang, Xiaofeng Zhang,
• Abstract summary: We propose Cadence: Diversity Recommendation via Causal Deconfounding of Co-purchase Relations and Counterfactual Exposure.<n>It is a plug-and-play framework built upon LightGCN to enhance recommendation diversity while preserving accuracy.<n>Our method consistently outperforms state-of-the-art diversity models in both diversity and accuracy.
• Score: 23.71990071700833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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