Product Rating Distribution Estimation using a LDL-Based Method with Uniform Manifold Approximation and Projection

Abstract

Ratings of products serve as a crucial indicator for assessing the impact of products in the retail market. Existing methods in rating estimation of product primarily use single-label machine learning methods, where the prediction may fail to represent the whole properties of products. This paper explores a challenging task to obtain product rating distribution estimation (RDE), which predict the distribution of product ratings instead of a single rating. Specifically, we focus on RDE of follower brands product, which provide relatively objective artifacts and easier to collect data. We formulate the RDE task based on a label distribution learning (LDL) framework, which uses the maximum entropy model functions as the output component of LDL, and generate the probability distribution for each category. However, one of the main challenge of conducting the RDE task within the LDL framework is that the large number of labels leads to an exponentially growing output space, which increases model complexity and reduces its performance. To address this problem, we propose a new model, called RDE-LDL, with an adaptive manifold learning module. The RDE-LDL method use uniform manifold approximation and projection (UMAP) to represent the label distribution manifold via fuzzy simplicial sets, which encodes label correlation information, and allows to regularize the maximum entropy model's output based on label correlation. Quantitative and qualitative experiments conducted on a marketing dataset verified the demonstrates the effectiveness of the RDE-LDL method with the UMAP-based module.