Dual Enhanced Meta-Learning With Adaptive Task Scheduler for Cold-Start Recommendation

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Dongxiao He;Jiaqi Cui;Xiaobao Wang;Guojie Song;Yuxiao Huang;Lingfei Wu
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引用次数: 0

Abstract

Recommendation systems typically rely on users’ historical behavior to infer their preferences. However, when new entries emerge, the system cannot make accurate prediction due to the lack of historical data. This is known as the “cold-start” problem, which not only limits the exposure of new items but also impacts the first experience of new users severely. Meta-learning has emerged as a promising approach to address this issue, but existing methods have limitations in dealing with the differences in user preferences and sparse monitoring data. To overcome these limitations, Dual enhanced Meta-learning with Adaptive Task Sampling is proposed. First, we propose an embedding enhancement strategy for cold nodes. Specifically, we map the cold-start embeddings into the warm space based on the common features shared across all nodes, and then add uniform noise to create the contrastive views. This strategy injects warm co-occurrence signals into the content of cold nodes, effectively enriching the feature space of cold nodes. Second, we introduce an adaptive task scheduler to measure the effectiveness of different meta-tasks and filter out the noise from invalid tasks. We assign different sampling probabilities to the tasks based on the learning process (gradient similarity) and the learning result (loss) of the meta-tasks. Finally, we consider the above two modules as auxiliary tasks for the main meta-model. Then, joint optimization is carried out through a multi-task learning framework. Experiments in three cold-start scenarios show that our approach outperforms the most advanced baselines, including traditional methods, HIN-based methods, and meta-learning-based methods.
基于自适应任务调度的双增强元学习冷启动推荐
推荐系统通常依赖于用户的历史行为来推断他们的偏好。但是,当新条目出现时,由于缺乏历史数据,系统无法做出准确的预测。这就是所谓的“冷启动”问题,这不仅限制了新项目的曝光,而且严重影响了新用户的首次体验。元学习已经成为解决这个问题的一种很有前途的方法,但是现有的方法在处理用户偏好的差异和稀疏的监测数据方面存在局限性。为了克服这些局限性,提出了具有自适应任务抽样的双增强元学习。首先,我们提出了一种冷节点嵌入增强策略。具体来说,我们基于所有节点共享的共同特征将冷启动嵌入映射到温暖空间,然后添加均匀噪声来创建对比视图。该策略在冷节点的内容中注入了暖共现信号,有效丰富了冷节点的特征空间。其次,我们引入了一个自适应任务调度程序来衡量不同元任务的有效性,并过滤掉无效任务中的噪声。我们根据元任务的学习过程(梯度相似度)和学习结果(损失)为任务分配不同的采样概率。最后,我们将上述两个模块视为主元模型的辅助任务。然后,通过多任务学习框架进行联合优化。在三种冷启动场景下的实验表明,我们的方法优于最先进的基线方法,包括传统方法、基于hin的方法和基于元学习的方法。
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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