Analyzing Customer Feedback for Product Fit Prediction
This work addresses the challenge of rare and text-based customer feedback for product fit prediction in e-commerce, though it is incremental as it focuses on comparing existing NLP methods without integrating the results into recommendation systems.
The paper tackled the problem of extracting product fit feedback from customer reviews to improve online fashion shopping recommendations, finding that the ULMFit transfer learning approach achieved the highest accuracy for this text classification task.
One of the biggest hurdles for customers when purchasing fashion online, is the difficulty of finding products with the right fit. In order to provide a better online shopping experience, platforms need to find ways to recommend the right product sizes and the best fitting products to their customers. These recommendation systems, however, require customer feedback in order to estimate the most suitable sizing options. Such feedback is rare and often only available as natural text. In this paper, we examine the extraction of product fit feedback from customer reviews using natural language processing techniques. In particular, we compare traditional methods with more recent transfer learning techniques for text classification, and analyze their results. Our evaluation shows, that the transfer learning approach ULMFit is not only comparatively fast to train, but also achieves highest accuracy on this task. The integration of the extracted information with actual size recommendation systems is left for future work.