Algorithmic content generation for products

Abstract

Content is one of the most essential parts of products on e-commerce websites such as eBay. It not only drives user-engagement but also traffic from various search engine websites based on the relevance. Generating the content for the products, however comes with a wide set of challenges, due to the complexity of commerce at scale, and requires new applications in text processing and information extraction to address some core issues. Some of the factors which need to be addressed are: scalability (millions of products), dynamism (products change with time), removal of item-specific or seller specific information (maintain generality), size of the content etc. Generally, curators are hired for writing the product descriptions manually, which is not cost-effective and is not scalable. In the current work, an algorithmic framework based on Natural Language Processing and Deep Learning is proposed and used to generate the content for ecommerce products. Seller descriptions for multiple items aggregated at a product level are used for content generation. Furthermore, a combination of behavioral and text signals such as search queries are also used to understand the user intent. Two different approaches are proposed in this work: Extraction (sentence retrieval) and Abstraction (sentence generation). The results of both the methods are analyzed and it is depicted that algorithmic content generation is scalable, fast and has potential to cut down the manualcuration cost dramatically.