Automated Product-Attribute Mapping

Abstract

Aggregate analysis, such as comparing country-wise sales versus global market share across product categories, is often complicated by the unavailability of common join attributes, e.g., category, across diverse datasets from different geographies or retail chains. Sometimes this is a missing data issue, while in other cases it may be inherent, e.g., the records in different geographical databases may actually describe different product ‘SKUs’, or follow different norms for categorization. Often a tedious manual mapping process is often employed in practice. We focus on improving such a process using machine-learning driven automation. Record linkage techniques, such as [5] can be used to automatically map products in different data sources to a common set of global attributes, thereby enabling federated aggregation joins to be performed. Traditional record-linkage techniques are typically unsupervised, relying textual similarity features across attributes to estimate matches. In this paper, we present an ensemble model combining minimal supervision using Bayesian network models together with unsupervised textual matching for automating such ‘attribute fusion’. We present results of our approach on a large volume of real-life data from a market-research scenario and compare with a standard record matching algorithm. Our approach is especially suited for practical implementation since we also provide confidence values for matches, enabling routing of items for human intervention where required.