arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.11842cs.IRcs.LG
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基于个性化自适应Meta学习的冷启动用户偏好预测

Personalized Adaptive Meta Learning for Cold-start User Preference Prediction

School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Alibaba Group（阿里巴巴集团）

Runsheng Yu, Yu Gong, Xu He, Bo An, Yu Zhu, Qingwen Liu, Wenwu Ou

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摘要：

个性化用户偏好预测中的一个常见挑战是冷启动问题。由于缺乏用户与项目之间的交互，直接从新用户的日志数据中学习会导致严重的过拟合问题。近年来，已有的研究将冷启动个性化偏好预测问题视为一个少样本学习问题，以用户为任务，以推荐项目为类，利用基于梯度的Meta学习方法（MAML）来解决这一问题.然而，在现实世界的应用中，用户不是均匀分布的（即，不同的用户可以具有不同的浏览历史、推荐项目和用户简档。我们将主要用户定义为具有大量用户共享相似用户信息的组中的用户，其他用户是次要用户），现有的MAML方法倾向于适合主要用户而忽略次要用户。为了解决这个冷启动任务过拟合问题，我们提出了一种新的个性化自适应Meta学习方法，同时考虑主要用户和次要用户，有三个关键贡献：1）我们是第一个提出了一个个性化的自适应学习率元学习方法，以提高MAML的性能，同时关注主要用户和次要用户。2)为了给每个用户提供更好的个性化学习率，我们引入了一种基于相似度的方法来寻找相似的用户作为参考，并引入了一种基于树的方法来存储用户的特征以进行快速搜索。3)为了减少内存使用，我们设计了一个与内存无关的正则化器，以进一步降低空间复杂度为常数，同时保持性能。MovieLens，BookCrossing和真实世界生产数据集上的实验表明，我们的方法对于次要用户和主要用户都显着优于最先进的方法。

英文摘要：

A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data causes serious over-fitting problem. Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge. However, in real-world application, the users are not uniformly distributed (i.e., different users may have different browsing history, recommended items, and user profiles. We define the major users as the users in the groups with large numbers of users sharing similar user information, and other users are the minor users), existing MAML approaches tend to fit the major users and ignore the minor users. To address this cold-start task-overfitting problem, we propose a novel personalized adaptive meta learning approach to consider both the major and the minor users with three key contributions: 1) We are the first to present a personalized adaptive learning rate meta-learning approach to improve the performance of MAML by focusing on both the major and minor users. 2) To provide better personalized learning rates for each user, we introduce a similarity-based method to find similar users as a reference and a tree-based method to store users' features for fast search. 3) To reduce the memory usage, we design a memory agnostic regularizer to further reduce the space complexity to constant while maintain the performance. Experiments on MovieLens, BookCrossing, and real-world production datasets reveal that our method outperforms the state-of-the-art methods dramatically for both the minor and major users.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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