Learning to Augment for Casual User Recommendation

Users who come to recommendation platforms are heterogeneous in activity levels. There usually exists a group of core users who visit the platform regularly and consume a large body of contents upon each visit, while others are casual users who tend to visit the platform occasionally and consume less each time. As a result, consumption activities from core users often dominate the training data used for learning. As core users can exhibit different activity patterns from casual users, recommender systems trained on historical user activity data usually achieve much worse performance on casual users than core users. To bridge the gap, we propose a model-agnostic framework \textit{L2Aug} to improve recommendations for casual users through data augmentation, without sacrificing core user experience. \textit{L2Aug} is powered by a data augmentor that learns to generate augmented interaction sequences, in order to fine-tune and optimize the performance of the recommendation system for casual users. On four real-world public datasets, the proposed \textit{L2Aug} outperforms other treatment methods and achieves the best sequential recommendation performance for both casual and core users. We also test \textit{L2Aug} in an online simulation environment with real-time feedback to further validate its efficacy, and showcase its flexibility in supporting different augmentation actions.

• Machine Intelligence

• Information Retrieval and the Web