Evolution of the online rating platform data structures and its implications for recommender systems

Hao Wang

doi:10.1117/12.2686575

28 July 2023 Evolution of the online rating platform data structures and its implications for recommender systems

Hao Wang

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127563Y (2023) https://doi.org/10.1117/12.2686575
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

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.

Citation Download Citation

Hao Wang "Evolution of the online rating platform data structures and its implications for recommender systems", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127563Y (28 July 2023); https://doi.org/10.1117/12.2686575

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