DiPS: Differentiable Policy for Sketching in Recommender Systems
DiPS: Differentiable Policy for Sketching in Recommender Systems
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DOI：
10.1609/aaai.v36i6.20625
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发表时间：
2021-12
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影响因子：
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通讯作者：
Aritra Ghosh;Saayan Mitra;Andrew S. Lan
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作者：
Aritra Ghosh;Saayan Mitra;Andrew S. Lan
在顺序推荐系统应用程序中，重要的是开发能够捕获用户随时间变化的兴趣的模型，以便成功地推荐他们可能与之交互的未来项目。对于历史悠久的用户，基于循环神经网络的典型模型往往会忘记很久以前的重要项目。最近的研究表明，存储过去项目的小草图可以改善顺序推荐任务。然而，这些工作都依赖于静态草图策略，即启发式方法来选择要保留在草图中的项目，这并不一定是最优的，也不能随着时间的推移随着更多的训练数据而改进。在本文中，我们提出了一种可微分的草图绘制策略（DiPS），该框架与推荐系统模型一起以端到端方式学习数据驱动的草图绘制策略，以明确地最大化未来的推荐质量。我们还提出了一个近似的梯度估计，用于优化计算效率高的草图算法参数。我们在各种实际设置下验证了DiPS在真实数据集上的有效性，并表明与现有的草图策略相比，它需要的草图项目最多减少50%才能达到相同的预测质量。
In sequential recommender system applications, it is important to develop models that can capture users' evolving interest over time to successfully recommend future items that they are likely to interact with. For users with long histories, typical models based on recurrent neural networks tend to forget important items in the distant past. Recent works have shown that storing a small sketch of past items can improve sequential recommendation tasks. However, these works all rely on static sketching policies, i.e., heuristics to select items to keep in the sketch, which are not necessarily optimal and cannot improve over time with more training data. In this paper, we propose a differentiable policy for sketching (DiPS), a framework that learns a data-driven sketching policy in an end-to-end manner together with the recommender system model to explicitly maximize recommendation quality in the future. We also propose an approximate estimator of the gradient for optimizing the sketching algorithm parameters that is computationally efficient. We verify the effectiveness of DiPS on real-world datasets under various practical settings and show that it requires up to 50% fewer sketch items to reach the same predictive quality than existing sketching policies.