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Online rating platform represents the new trend of online cultural andcommercial goods consumption. The user rating data on such platforms are foodsfor recommender system algorithms. Understanding the evolution pattern and itsunderlying mechanism is the key to understand the structures of input data forrecommender systems. Prior research on input data analysis for recommendersystems 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 theinput data structures. We discover that homogeneous Poisson Process could notcapture the mechanism of user rating behavior on online rating platforms, andinhomogeneous Poisson Process is compatible with the formation process.

Evolution of the Online Rating Platform Data Structures and its Implications for Recommender Systems