Diverse Negative Sampling for Implicit Collaborative Filtering

• URL: http://arxiv.org/abs/2508.14468v1
• Date: Wed, 20 Aug 2025 06:48:54 GMT
• Title: Diverse Negative Sampling for Implicit Collaborative Filtering
• Authors: Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan,
• Abstract summary: We propose Diverse Negative Sampling (DivNS) to account for diversity in negative training data.<n>DivNS finds hard negative items with large preference scores and constructs user-specific caches.<n>It selects a diverse subset of negative items from the cache while ensuring dissimilarity from the user's hard negatives.<n>The resulting synthetic negatives are both informative and diverse, enabling recommenders to learn a broader item space and improve their generalisability.
• Score: 26.57812122315108
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Implicit collaborative filtering recommenders are usually trained to learn user positive preferences. Negative sampling, which selects informative negative items to form negative training data, plays a crucial role in this process. Since items are often clustered in the latent space, existing negative sampling strategies normally oversample negative items from the dense regions. This leads to homogeneous negative data and limited model expressiveness. In this paper, we propose Diverse Negative Sampling (DivNS), a novel approach that explicitly accounts for diversity in negative training data during the negative sampling process. DivNS first finds hard negative items with large preference scores and constructs user-specific caches that store unused but highly informative negative samples. Then, its diversity-augmented sampler selects a diverse subset of negative items from the cache while ensuring dissimilarity from the user's hard negatives. Finally, a synthetic negatives generator combines the selected diverse negatives with hard negatives to form more effective training data. The resulting synthetic negatives are both informative and diverse, enabling recommenders to learn a broader item space and improve their generalisability. Extensive experiments on four public datasets demonstrate the effectiveness of DivNS in improving recommendation quality while maintaining computational efficiency.

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