Click-Through Rate (CTR) prediction on cold users is a challenging task in recommender systems. Recent researches have resorted to meta-learning to tackle the cold-user challenge, which either perform few-shot user representation learning or adopt optimization-based meta-learning. However, existing methods suffer from information loss or inefficient optimization process, and they fail to explicitly model global user preference knowledge which is crucial to complement the sparse and insufficient preference information of cold users. In this paper, we propose a novel and efficient approach named RESUS, which decouples the learning of global preference knowledge contributed by collective users from the learning of residual preferences for individual users. Specifically, we employ a shared predictor to infer basis user preferences, which acquires global preference knowledge from the interactions of different users. Meanwhile, we develop two efficient algorithms based on the nearest neighbor and ridge regression predictors, which infer residual user preferences via learning quickly from a few user-specific interactions. Extensive experiments on three public datasets demonstrate that our RESUS approach is efficient and effective in improving CTR prediction accuracy on cold users, compared with various state-of-the-art methods.
翻译:在推荐者系统中,冷冻用户的点击浏览率(CTR)预测是一项具有挑战性的任务。最近的研究采用元学习方法来应对冷冷用户的挑战,这种方法要么进行少见的用户代表学习,要么采用基于优化的元学习。然而,现有方法存在信息丢失或效率低下的优化过程,它们未能明确模拟全球用户偏好知识,而这种知识对于补充冷冷用户稀少和不充分的偏好信息至关重要。在本文件中,我们提出了一个名为RESUS的新颖而有效的方法,它使集体用户对全球偏爱知识的学习与对个别用户的剩余偏好学习脱钩。具体地说,我们使用一个共享的预测器来推断用户偏好,从不同用户的互动中获取全球偏好知识。与此同时,我们根据最近的邻居和山脊回归预测器开发了两种高效的算法,通过快速学习一些用户特有的互动来推断用户的剩余偏好。在三个公共数据集上进行的广泛实验表明,我们对冷用户的CTR预测方法与各种状态方法相比是高效和有效的。