“Unbiasedness,” which is an important property to ensure that users’ ratings indeed reflect their true evaluations of products, is vital both in shaping consumer purchase decisions and providing reliable recommendations in online rating systems. Recent experimental studies showed that distortions from historical ratings would ruin the unbiasedness of subsequent ratings. How to “discover” historical distortions in each single rating (or at the micro-level), and perform the “debiasing operations” are our main objective. Using 42M real customer ratings, we first show that users either “assimilate” or “contrast” to historical ratings under different scenarios, which can be further explained by a well-known psychological argument: the “Assimilate-Contrast” theory. This motivates us to propose the Historical Influence Aware Latent Factor Model (HIALF), the “first” model for real rating systems to capture and mitigate historical distortions in each single rating. HIALF allows us to study the influence patterns of historical ratings from a modelling perspective, which perfectly matches the assimilation and contrast effects observed in experiments. Moreover, HIALF achieves significant improvements in predicting subsequent ratings and characterizing relationships in ratings. It also contributes to better recommendations, wiser consumer purchase decisions, and deeper understanding of historical distortions in both honest rating and misbehaving rating settings.

中文翻译：

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了解在线评级系统中的同化对比效应

“无偏见”是确保用户评分确实反映他们对产品的真实评价的一项重要属性，对于塑造消费者购买决策和在在线评分系统中提供可靠的建议都至关重要。最近的实验研究表明，历史评级的扭曲会破坏后续评级的公正性。如何“发现”每个单一评级（或微观层面）的历史扭曲，并进行“去偏操作”是我们的主要目标。使用 4200 万真实客户评分，我们首先表明用户在不同场景下对历史评分要么“同化”，要么“对比”，这可以用一个著名的心理学论点进一步解释：“同化-对比”理论。这促使我们提出历史影响感知潜在因素模型（HIALF），真实评级系统的“第一”模型，用于捕捉和减轻每个单一评级中的历史扭曲。HIALF 使我们能够从建模的角度研究历史评级的影响模式，这与实验中观察到的同化和对比效应完美匹配。此外，HIALF 在预测后续评级和表征评级关系方面取得了显着改进。它还有助于提供更好的建议、更明智的消费者购买决策，以及更深入地了解诚实评级和行为不端评级设置中的历史扭曲。这与实验中观察到的同化和对比效果完美匹配。此外，HIALF 在预测后续评级和表征评级关系方面取得了显着改进。它还有助于提供更好的建议、更明智的消费者购买决策，以及更深入地了解诚实评级和行为不端评级设置中的历史扭曲。这与实验中观察到的同化和对比效果完美匹配。此外，HIALF 在预测后续评级和表征评级关系方面取得了显着改进。它还有助于提供更好的建议、更明智的消费者购买决策，以及更深入地了解诚实评级和行为不端评级设置中的历史扭曲。