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KGNS: Knowledge graph-driven neighbor selection for long-tail recommendations
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DOI：10.1016/j.ipm.2025.104561.png)
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Abstract
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Long-tail recommendations face persistent challenges due to popularity bias in user interaction data and sparse interactions for long-tail items. Existing knowledge graph (KG)-based approaches often amplify these issues through indiscriminate neighborhood selection, leading to biased user representations and noisy item embeddings, which ultimately result in a trade-off between accuracy and diversity. To address these limitations, we propose KGNS, a KG-driven neighbor selection approach that strategically reconstructs neighborhoods for both users and items by leveraging rich KG semantics. On the user side, KGNS employs a long-tail neighbor selector to identify semantically relevant long-tail items, reconstructing user neighborhoods to mitigate popularity bias and better capture genuine long-tail interests. On the item side, a co-occurrence neighbor selector enhances long-tail item embeddings by introducing high-quality, semantically correlated neighbors without introducing noise. Through multi-task training, KGNS optimizes the model to recommend top N items that balance both mainstream and long-tail preferences. Extensive experiments on three real-world datasets demonstrate that KGNS not only enhances long-tail recommendation performance but also maintains high overall accuracy, achieving an average improvement of 6.75 % in accuracy and 3.85 % in diversity over state-of-the-art baselines. The code is available at: https://github.com/ZZP-RS/KGNS .
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