ForeSeer：使用时间图嵌入进行产品方面预测
ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding
AI总结：
针对缺少评论的新产品未来方面预测问题，提出在时间产品图上渐进训练的 ForeSeer，融合评论文本、产品网络与时间信息，显著提升方面及链接预测性能。
摘要：
由于理解客户需求和产品属性的重要性，开发文本挖掘方法以从客户评论中挖掘方面已得到充分研究。相比之下，目前尚不清楚如何预测当前几乎没有评论信息的新产品未来会出现的方面。这项任务被我们命名为产品方面预测，对于推荐新产品至关重要，但由于评论缺失也具有挑战性。在此，我们提出 ForeSeer，这是一种新颖的文本挖掘和产品嵌入方法，在时间产品图上进行渐进式训练，用于这一新颖的产品方面预测任务。ForeSeer 从大型产品图上的相似产品迁移评论，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是联合提供评论、产品和方面嵌入，这些嵌入既具有时间敏感性，又较少受到极度不平衡的方面频率的影响。我们在一个真实世界的产品评论系统上评估了 ForeSeer，该系统包含 3 年间的 11,536,382 条评论和 11,000 个产品。我们观察到，在未给定方面关联的真实设置下，ForeSeer 显著优于现有方法，AUPRC 至少提升 49.1%。ForeSeer 进一步改进了产品图上的未来链接预测以及评论方面关联预测。总体而言，ForeSeer 通过有效整合评论文本、产品网络和时间信息，提供了一个用于评论预测的新颖框架，为在线购物推荐和电子商务应用开辟了新途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
