A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handle situations with new users or new items. Recently, some works introduce the meta-optimization idea into the recommendation scenarios, i.e. predicting the user preference by only a few of past interacted items. The core idea is learning a global sharing initialization parameter for all users and then learning the local parameters for each user separately. However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users. In this paper, we design two memory matrices that can store task-specific memories and feature-specific memories. Specifically, the feature-specific memories are used to guide the model with personalized parameter initialization, while the task-specific memories are used to guide the model fast predicting the user preference. And we adopt a meta-optimization approach for optimizing the proposed method. We test the model on two widely used recommendation datasets and consider four cold-start situations. The experimental results show the effectiveness of the proposed methods.

中文翻译：

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MAMO：用于冷启动推荐的内存增强元优化

大多数当前推荐系统的共同挑战是冷启动问题。由于缺乏用户-项目交互，微调的推荐系统无法处理新用户或新项目的情况。最近，一些工作将元优化思想引入到推荐场景中，即仅通过少数过去的交互项目来预测用户偏好。核心思想是为所有用户学习一个全局共享初始化参数，然后为每个用户分别学习局部参数。然而，大多数基于元学习的推荐方法采用与模型无关的元学习进行参数初始化，其中全局共享参数可能会将模型引导到某些用户的局部最优。在本文中，我们设计了两个记忆矩阵，可以存储特定任务的记忆和特定于特征的记忆。具体而言，特征特定记忆用于指导模型进行个性化参数初始化，而任务特定记忆用于指导模型快速预测用户偏好。我们采用元优化方法来优化所提出的方法。我们在两个广泛使用的推荐数据集上测试模型并考虑四种冷启动情况。实验结果表明了所提出方法的有效性。我们采用元优化方法来优化所提出的方法。我们在两个广泛使用的推荐数据集上测试模型并考虑四种冷启动情况。实验结果表明了所提出方法的有效性。我们采用元优化方法来优化所提出的方法。我们在两个广泛使用的推荐数据集上测试模型并考虑四种冷启动情况。实验结果表明了所提出方法的有效性。