The Upside of Bias: Personalizing Long-Tail Item Recommendations with Biased Sampling

Recommendation systems drive user engagement across social media, streaming platforms, and e-commerce by learning from past interactions. The relevance of a recommended item depends on the quality of the user and item embeddings learned by the model. However, interactions across items follow a highly skewed distribution. Many items receive very few interactions, while a small subset dominates user engagement, which is known as the long-tail problem. Since recommendation models learn from user-item interactions, this imbalance reinforces model bias, limiting exposure for tail items and reducing recommendation diversity. To address this challenge, we introduced Biased User History Synthesis in an earlier work, a training algorithm designed to address this issue while enhancing personalization. Our method leverages a sampling strategy that prioritizes tail items in user interaction histories, then use them to augment user representations. Our approach increases tail item visibility and generates more personalized user embeddings, improving recommendation performance across both head and tail items. This article extends our earlier work with additional details on the models and related work, and new contributions including broader evaluation on another dataset, deeper fairness and efficiency analysis, and enhanced ablation and visualization results. The source code is available at https://github.com/lkp411/BiasedUserHistorySynthesis.