RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction
This addresses the cold-start problem for users in recommender systems, but it is incremental as it builds on existing meta-learning approaches.
The paper tackled the problem of click-through rate prediction for cold users in recommender systems by proposing RESUS, which decouples global preference knowledge from residual user preferences, resulting in improved accuracy compared to state-of-the-art methods on three public datasets.
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.