Accurate Customer Address Matching via Weak Supervision for Geocode Learning
Arpan Paul, Saket Maheshwary, Saurabh Sohoney · 2024
Determining the precise location of customers is important for an efficient and reliable delivery experience, both for customers and delivery associates. Address text is a primary source of information provided by customers about their location. In this paper, we study the important and challenging task of matching free-form customer address text to determine if two addresses represent the same physical building. We introduce a novel address matching framework that leverages transformer-based encoder to prevent tedious and time-consuming efforts spent on manual feature engineering by the baseline model. Furthermore, our proposed framework employs weak supervision to leverage historic delivery information and generate high-quality labeled data. This reduces the requirement for massive amounts of labeled data, typically needed for transformer-based models. Our experiments on manually curated datasets demonstrate the effective and generic nature of our approach, as we achieve 15.57% improvement in recall at 95% precision, on average, compared to the current baseline model across four geographies. We also introduce delivery point (DP) geocode learning for cold-start addresses as a downstream application of customer address matching. In addition to offline experiments, we performed online A/B experiments for DP geocode learning with our proposed approach and observed delivery precision improved by 8.09% and delivery defects reduced by 11.78% on average across four geographies in comparison to the baseline model.