论文标题
多目标推荐系统的多速率下降
Multi-Gradient Descent for Multi-Objective Recommender Systems
论文作者
论文摘要
推荐系统需要反映它们所应用的环境的复杂性。我们越了解可能受益的用户，推荐系统的目标越多。此外，除法律和道德限制外，还可能还有多个利益相关者 - 卖方，买家，股东。到目前为止，与具有相同规模的相关性且不相关的多种目标优化，到目前为止已被证明很困难。 我们引入了推荐系统（MGDREC）的随机多速率下降方法来解决此问题。我们表明，这超过了传统客观混合物（如收入和召回）中最先进的方法。不仅如此，通过梯度归一化，我们可以将具有多种尺度的目标从根本上结合到一个连贯的框架中。我们表明，不相关的目标，例如优质产品的比例，可以与准确性一起改进。通过使用随机性，我们避免了计算完整梯度的陷阱，并为其适用性提供了清晰的设置。
Recommender systems need to mirror the complexity of the environment they are applied in. The more we know about what might benefit the user, the more objectives the recommender system has. In addition there may be multiple stakeholders - sellers, buyers, shareholders - in addition to legal and ethical constraints. Simultaneously optimizing for a multitude of objectives, correlated and not correlated, having the same scale or not, has proven difficult so far. We introduce a stochastic multi-gradient descent approach to recommender systems (MGDRec) to solve this problem. We show that this exceeds state-of-the-art methods in traditional objective mixtures, like revenue and recall. Not only that, but through gradient normalization we can combine fundamentally different objectives, having diverse scales, into a single coherent framework. We show that uncorrelated objectives, like the proportion of quality products, can be improved alongside accuracy. Through the use of stochasticity, we avoid the pitfalls of calculating full gradients and provide a clear setting for its applicability.
