Summary of Analytical and Empirical Study Of Herding Effects in Recommendation Systems, by Hong Xie et al.
Analytical and Empirical Study of Herding Effects in Recommendation Systems
by Hong Xie, Mingze Zhong, Defu Lian, Zhen Wang, Enhong Chen
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a mathematical framework for managing online product ratings to correct herding effects, which can lead to misleading assessments. The authors develop a model characterizing herding factors, identify sufficient conditions for the historical collective opinion to converge to ground-truth quality, and quantify the speed of convergence. They prove that herding effects slow down convergence while accurate review selection mechanisms can accelerate it. Experiments on Amazon and TripAdvisor datasets demonstrate that proper rating aggregation rules can improve convergence speed by 41% and 62%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to fix a problem with online ratings. When many people rate something, their opinions can influence each other, making the average rating not very accurate. The authors create a math model to understand this effect and find ways to make it better. They show that if we choose the right rules for combining old ratings and selecting reviews, we can get closer to the real product quality. This is important because online shopping relies on these ratings. |
