Online sellers and advertisers are recommended keyphrases for their listed products, which they bid on to enhance their sales. One popular paradigm that generates such recommendations is Extreme Multi-Label Classification (XMC), which involves tagging/mapping keyphrases to items. We outline the limitations of training XMC models on click data for keyphrase recommendations on E-Commerce platforms. We introduce GraphEx, an innovative graph-based approach that recommends keyphrases to sellers using extraction of token permutations from item titles. Additionally, we demonstrate traditional metrics such as precision/recall isn't reliable on click-based data in practical applications, thereby necessitating a robust framework to evaluate performance in real-world scenarios. Our evaluation is designed to assess the relevance of keyphrases to items and the potential for buyer outreach. GraphEx outperforms production models at eBay, achieving the objectives mentioned above. It supports near real-time inferencing in resource-constrained production environments and scales effectively for billions of items.

We consider a supervised classification problem of categorizing e-commerce products based on just the words in the title. If done in real-time, the categorization can greatly benefit sellers by enabling them to offer immediate feedback. We present a deterministic algorithm by constructing weighted word co-occurrence graphs from the listing/item titles. We empirically evaluate this algorithm on two publicly available product listing datasets, Etsy and Amazon. Our method’s accuracy is comparable to that of a supervised classifier constructed using the fastText library. The inference time of our model is up to 2.9× faster than the fastText classifier and has small training times. The training and inference of our model scales well for big datasets performing large-scale classification on millions of listings. We perform a detailed analysis and provide insights into our method and the product categorization task.