Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instances with uniform distribution, which, however, will severely affect a model’s convergence, stability, and even recommendation accuracy. A promising solution for this problem is to over-sample the “difficult” (a.k.a. informative) instances that contribute more on training. But this will increase the risk of biasing the model and leading to non-optimal results. Moreover, existing samplers are either heuristic, which require domain knowledge and often fail to capture real “difficult” instances, or rely on a sampler model that suffers from low efficiency. To deal with these problems, we propose CoSam, an efficient and effective collaborative sampling method that consists of (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency, and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling. Correspondingly, we derive a fast reinforced training algorithm of our framework to boost the sampler performance and sampler-recommender collaboration. Extensive experiments on four real-world datasets demonstrate the superiority of the proposed collaborative sampler model and integrated sampler-recommender framework.

中文翻译：

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CoSam：用于推荐的高效协作自适应采样器

采样策略已广泛应用于许多推荐系统，以加速从隐式反馈数据中学习模型。一个典型的策略是抽取分布均匀的负样本，但是这会严重影响模型的收敛性、稳定性，甚至是推荐的准确性。这个问题的一个有希望的解决方案是对那些对训练有更多贡献的“困难”（又名信息丰富）实例进行过度采样。但这会增加使模型产生偏差并导致非最佳结果的风险。此外，现有的采样器要么是启发式的，需要领域知识并且通常无法捕获真正的“困难”实例，要么依赖于效率低下的采样器模型。为了解决这些问题，我们提出 CoSam，一种高效且有效的协作采样方法，包括 (1) 协作采样器模型，该模型在采样概率中明确利用用户-项目交互信息，并表现出良好的归一化、自适应、交互信息感知和采样效率特性，以及 (2)集成的采样器-推荐框架，利用采样器模型进行预测，以抵消不均匀采样引起的偏差。相应地，我们推导出了我们框架的快速强化训练算法，以提高采样器性能和采样器-推荐器协作。在四个真实世界数据集上的广泛实验证明了所提出的协作采样器模型和集成采样器推荐框架的优越性。