Online rating platform represents the new trend of online cultural and commercial goods consumption. The user rating data on such platforms are foods for recommender system algorithms. Understanding the evolution pattern and its underlying mechanism is the key to understand the structures of input data for recommender systems. Prior research on input data analysis for recommender systems is quite limited, with a notable exception in 2018 [6]. In this paper, we take advantage of Poisson Process to analyze the evolution mechanism of the input data structures. We discover that homogeneous Poisson Process could not capture the mechanism of user rating behavior on online rating platforms, and inhomogeneous Poisson Process is compatible with the formation process.

翻译：摘要：在线评分平台代表了在线文化和商业商品消费的新趋势。这些平台上的用户评价数据是推荐系统算法的食粮。了解其演变模式及其潜在机制是理解推荐系统输入数据结构的关键。以往关于推荐系统输入数据分析的研究相当有限，在2018年有一个值得注意的例外[6]。本文利用Poisson过程分析了输入数据结构的演变机制。我们发现均匀Poisson过程无法捕捉在线评分平台上用户评价行为的机制，而不均匀Poisson过程与形成过程相兼容。