ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding
Zixuan Liu Gaurush Hiranandani Kun Qian Eddie W. Huang Yi Xu Belinda Zeng Karthik Subbian Sheng Wang
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.