Low-Rank Online Dynamic Assortment with Dual Contextual Information
针对电商平台实时个性化推荐问题，提出一种低秩动态品类模型，利用用户和商品双重特征，通过高效算法平衡探索与利用，显著降低遗憾界，并在Expedia数据集上验证效果。
As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to continuously optimize assortments over time. In this paper, we consider the dynamic assortment problem with dual contexts – user and item features. In high-dimensional scenarios, the quadratic growth of dimensions complicates computation and estimation. To tackle this challenge, we introduce a new low-rank dynamic assortment model to transform this problem into a manageable scale. Then we propose an efficient algorithm that estimates the intrinsic subspaces and utilizes the upper confidence bound approach to address the exploration-exploitation trade-off in online decision making. Theoretically, we establish a regret bound of O˜((d1+d2)rT), where d1,d2 represent the dimensions of the user and item features respectively, r is the rank of the parameter matrix, and T denotes the time horizon. This bound represents a substantial improvement over prior literature, achieved by leveraging the low-rank structure. Extensive simulations and an application to the Expedia hotel recommendation dataset further demonstrate the advantages of our proposed method.