Diverse Negative Sampling for Implicit Collaborative Filtering

2025-08-20

AI Summary

In implicit collaborative filtering, existing negative sampling strategies tend to oversample in dense regions of user-item interactions, leading to homogeneous negative instances that impair model expressiveness and generalization. To address this, we propose DivNS—a novel negative sampling framework that explicitly models diversity for the first time. DivNS comprises three stages: user-specific cache management, diversity-aware sampling, and synthetic negative instance generation—collectively enhancing coverage breadth and discriminative power of negative samples. It requires no additional annotations and seamlessly integrates with mainstream implicit CF models. Extensive experiments on four public benchmarks demonstrate that DivNS consistently improves Recall@K and NDCG@K by an average of 3.2% over strong baselines—including BPR, IPS, and SLIME—while maintaining computationally efficient overhead.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

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.

Problem

• Addresses homogeneous negative data in implicit collaborative filtering
• Improves model expressiveness through diverse negative sampling
• Enhances recommendation quality by broadening learned item space

Innovation

• Diverse negative sampling from user-specific caches
• Combining hard negatives with diverse synthetic negatives
• Ensuring dissimilarity from user's hard negative items