Numerous algorithms have been developed for online product rating prediction, but the specific influence of user and product information in determining the final prediction score remains largely unexplored. Existing research often relies on narrowly defined data settings, which overlooks real-world challenges such as the cold-start problem, cross-category information utilization, and scalability and deployment issues. To delve deeper into these aspects, and particularly to uncover the roles of individual user taste and collective wisdom, we propose a unique and practical approach that emphasizes historical ratings at both the user and product levels, encapsulated using a continuously updated dynamic tree representation. This representation effectively captures the temporal dynamics of users and products, leverages user information across product categories, and provides a natural solution to the cold-start problem. Furthermore, we have developed an efficient data processing strategy that makes this approach highly scalable and easily deployable. Comprehensive experiments in real industry settings demonstrate the effectiveness of our approach. Notably, our findings reveal that individual taste dominates over collective wisdom in online product rating prediction, a perspective that contrasts with the commonly observed wisdom of the crowd phenomenon in other domains. This dominance of individual user taste is consistent across various model types, including the boosting tree model, recurrent neural network (RNN), and transformer-based architectures. This observation holds true across the overall population, within individual product categories, and in cold-start scenarios. Our findings underscore the significance of individual user tastes in the context of online product rating prediction and the robustness of our approach across different model architectures.

翻译：尽管已有众多算法被开发用于在线产品评分预测，但用户和产品信息在决定最终预测分数中的具体影响在很大程度上仍未得到充分探索。现有研究通常依赖于狭义定义的数据设置，忽视了现实世界中的挑战，如冷启动问题、跨类别信息利用以及可扩展性和部署问题。为了更深入地探讨这些方面，特别是揭示个体用户品味和集体智慧的作用，我们提出了一种独特而实用的方法，强调在用户和产品两个层面的历史评分，并使用持续更新的动态树表示进行封装。这种表示有效地捕捉了用户和产品的时间动态，利用跨产品类别的用户信息，并为冷启动问题提供了自然的解决方案。此外，我们开发了一种高效的数据处理策略，使该方法具有高度可扩展性且易于部署。在真实工业环境中的综合实验证明了我们方法的有效性。值得注意的是，我们的研究结果表明，在在线产品评分预测中，个体品味的主导作用超过了集体智慧，这一观点与其他领域中常见的群体智慧现象形成了对比。个体用户品味的主导地位在不同模型类型中均保持一致，包括提升树模型、循环神经网络（RNN）以及基于Transformer的架构。这一观察结果在总体人群中、单个产品类别内以及冷启动场景下均成立。我们的发现强调了在在线产品评分预测背景下个体用户品味的重要性，以及我们方法在不同模型架构中的鲁棒性。