In the vast landscape of internet information, recommender systems (RecSys) have become essential for guiding users through a sea of choices aligned with their preferences. These systems have applications in diverse domains, such as news feeds, game suggestions, and shopping recommendations. Personalization is a key technique in RecSys, where modern methods leverage representation learning to encode user/item interactions into embeddings, forming the foundation for personalized recommendations. However, integrating information from multiple sources to enhance recommendation performance remains challenging. This paper introduces a novel approach named PMTRec, the first personalized multi-task learning algorithm to obtain comprehensive user/item embeddings from various information sources. Addressing challenges specific to personalized RecSys, we develop modules to handle personalized task weights, diverse task orientations, and variations in gradient magnitudes across tasks. PMTRec dynamically adjusts task weights based on gradient norms for each user/item, employs a Task Focusing module to align gradient combinations with the main recommendation task, and uses a Gradient Magnitude Balancing module to ensure balanced training across tasks. Through extensive experiments on three real-world datasets with different scales, we demonstrate that PMTRec significantly outperforms existing multi-task learning methods, showcasing its effectiveness in achieving enhanced recommendation accuracy by leveraging multiple tasks simultaneously. Our contributions open new avenues for advancing personalized multi-task training in recommender systems.

翻译：在互联网信息的广阔领域中，推荐系统已成为引导用户浏览符合其偏好海量选择的关键工具。这类系统在新闻推送、游戏推荐和购物建议等多个领域均有应用。个性化是推荐系统中的核心技术，现代方法通过表征学习将用户/物品交互编码为嵌入向量，为个性化推荐奠定基础。然而，如何整合多源信息以提升推荐性能仍是挑战。本文提出名为PMTRec的创新方法，这是首个从多样化信息源获取全面用户/物品嵌入的个性化多任务学习算法。针对个性化推荐系统的特定挑战，我们开发了处理个性化任务权重、多样化任务导向以及跨任务梯度幅度变化的模块。PMTRec基于每个用户/物品的梯度范数动态调整任务权重，采用任务聚焦模块使梯度组合与主推荐任务对齐，并利用梯度幅度平衡模块确保跨任务的均衡训练。通过在三个不同规模的真实数据集上进行大量实验，我们证明PMTRec显著优于现有多任务学习方法，展示了其通过同时利用多任务实现推荐精度提升的有效性。我们的研究为推进推荐系统中个性化多任务训练开辟了新途径。