Analytical and Empirical Study of Herding Effects in Recommendation Systems
<|reference_start|>Analytical and Empirical Study of Herding Effects in Recommendation Systems: Online rating systems are often used in numerous web or mobile applications, e.g., Amazon and TripAdvisor, to assess the ground-truth quality of products. Due to herding effects, the aggregation of historical ratings (or historical collective opinion) can significantly influence subsequent ratings, leading to misleading and erroneous assessments. We study how to manage product ratings via rating aggregation rules and shortlisted representative reviews, for the purpose of correcting the assessment error. We first develop a mathematical model to characterize important factors of herding effects in product ratings. We then identify sufficient conditions (via the stochastic approximation theory), under which the historical collective opinion converges to the ground-truth collective opinion of the whole user population. These conditions identify a class of rating aggregation rules and review selection mechanisms that can reveal the ground-truth product quality. We also quantify the speed of convergence (via the martingale theory), which reflects the efficiency of rating aggregation rules and review selection mechanisms. We prove that the herding effects slow down the speed of convergence while an accurate review selection mechanism can speed it up. We also study the speed of convergence numerically and reveal trade-offs in selecting rating aggregation rules and review selection mechanisms. To show the utility of our framework, we design a maximum likelihood algorithm to infer model parameters from ratings, and conduct experiments on rating datasets from Amazon and TripAdvisor. We show that proper recency aware rating aggregation rules can improve the speed of convergence in Amazon and TripAdvisor by 41% and 62% respectively.<|reference_end|>
@article{xie2024analytical,
    title={Analytical and Empirical Study of Herding Effects in Recommendation
  Systems},
    author={Hong Xie, Mingze Zhong, Defu Lian, Zhen Wang, Enhong Chen},
    journal={arXiv preprint arXiv:2408.10895},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10895},
    primaryClass={cs.AI}
}