2023/08/03 by Nikhil Mehta, Mehta, Nikhil, Anima Singh +9
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning in Healthcare #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.2308.01563
openalex publication_date 2023/08/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Using multiple user representations (MUR) to model user behavior instead of a single user representation (SUR) has been shown to improve personalization in recommendation systems. However, the performance gains observed with MUR can be sensitive to the skewness in the item and/or user interest distribution. When the data distribution is highly skewed, the gains observed by learning multiple representations diminish since the model dominates on head items/interests, leading to poor performance on tail items. Robustness to data sparsity is therefore essential for MUR-based approaches to achieve good performance for recommendations. Yet, research in MUR and data imbalance have largely been done independently. In this paper, we delve deeper into the shortcomings of MUR inferred from imbalanced data distributions. We make several contributions: (1) Using synthetic datasets, we demonstrate the sensitivity of MUR with respect to data imbalance, (2) To improve MUR for tail items, we propose an iterative density weighting scheme (IDW) with user tower calibration to mitigate the effect of training over long-tail distribution on personalization, and (3) Through extensive experiments on three real-world benchmarks, we demonstrate IDW outperforms other alternatives that address data imbalance.