The Title Says It All

Abstract

Search relevance is a very critical component in e-commerce applications. One of the strongest signals that determine the relevance of an item listing to an e-commerce query is the title of the item. Traditional methods for capturing this signal compare words in listing titles and the user query using tf-idf scores, or use a machine learned model with words as features and target clicks or relevance labels. Contrary to these approaches, we build a parameterized model to determine the weights of popular title terms for a query and then use these title term weights to compute the relevance of a listing title to the query. For this, we use human judged binary relevance labels of query and item title pairs as labeled data and train a model leveraging a variety of features to learn these query specific title term weights. We propose two novel approaches to model these title term weights using the relevance target and explore several novel features specific to e-commerce for this term weighting model. We use the resulting title relevance score as a feature in eBay's machine learned ranker for e-commerce search serving millions of queries each day. We observe a significant improvement over a baseline click-based binary independence model for capturing item title relevance in several metrics including model accuracy and overall relevance and engagement observed through A/B testing. We also experimentally illustrate that this feature optimized for relevance works well in conjunction with textual features optimized for demand.