Developing text mining approaches to mine aspects from customer reviews has been well-studied due to its importance in understanding customer needs and product attributes. In contrast, it remains unclear how to predict the future emerging aspects of a new product that currently has little review information. This task, which we named product aspect forecasting, is critical for recommending new products, but also challenging because of the missing reviews. Here, we propose ForeSeer, a novel textual mining and product embedding approach progressively trained on temporal product graphs for this novel product aspect forecasting task. ForeSeer transfers reviews from similar products on a large product graph and exploits these reviews to predict aspects that might emerge in future reviews. A key novelty of our method is to jointly provide review, product, and aspect embeddings that are both time-sensitive and less affected by extremely imbalanced aspect frequencies. We evaluated ForeSeer on a real-world product review system containing 11,536,382 reviews and 11,000 products over 3 years. We observe that ForeSeer substantially outperformed existing approaches with at least 49.1\% AUPRC improvement under the real setting where aspect associations are not given. ForeSeer further improves future link prediction on the product graph and the review aspect association prediction. Collectively, Foreseer offers a novel framework for review forecasting by effectively integrating review text, product network, and temporal information, opening up new avenues for online shopping recommendation and e-commerce applications.

翻译：从客户评论中挖掘方面信息的文本挖掘方法因其在理解客户需求和产品属性中的重要性而得到了充分研究。相比之下，如何预测当前评论信息较少的未来新兴产品的方面仍不明确。我们将此任务命名为产品方面预测，它对推荐新产品至关重要，但由于缺乏评论而具有挑战性。为此，我们提出ForeSeer，一种新颖的文本挖掘与产品嵌入方法，该方法在时间产品图上逐步训练，用于这一新颖的产品方面预测任务。ForeSeer通过大型产品图从相似产品中迁移评论，并利用这些评论预测未来评论中可能出现的方面。我们方法的一个关键创新点是联合提供评论、产品和方面的嵌入，这些嵌入既具有时间敏感性，又受极端不平衡的方面频率影响较小。我们在一个包含3年内11,536,382条评论和11,000个产品的真实产品评论系统上评估了ForeSeer。我们观察到，在方面关联未给定的真实场景下，ForeSeer的性能显著优于现有方法，AUPRC至少提升了49.1%。此外，ForeSeer进一步改进了产品图上的未来链接预测和评论方面关联预测。总体而言，ForeSeer通过有效整合评论文本、产品网络和时间信息，为评论预测提供了一种新颖框架，为在线购物推荐和电子商务应用开辟了新途径。