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：在线评分平台代表了在线文化及商业商品消费的新趋势。此类平台上的用户评分数据是推荐系统算法的"食粮"。理解其演变模式及潜在机制，是掌握推荐系统输入数据结构的关键。此前对推荐系统输入数据的分析研究十分有限，仅2018年的文献[6]是显著例外。本文利用泊松过程分析输入数据结构的演变机制。我们发现，齐次泊松过程无法捕捉在线评分平台用户评分行为的机制，而非齐次泊松过程与该形成过程相吻合。