We describe our work on extracting attribute and value pairs from textual product descriptions. The goal is to augment databases of products by representing each product as a set of attribute-value pairs. Such a representation is beneficial for tasks where treating the product as a set of attribute-value pairs is more useful than as an atomic entity. Examples of such applications include demand forecasting, assortment optimization, product recommendations, and assortment comparison across retailers and manufacturers. We deal with both implicit and explicit attributes and formulate both kinds of extractions as classification problems. Using single-view and multi-view semi-supervised learning algorithms, we are able to exploit large amounts of unlabeled data present in this domain while reducing the need for initial labeled data that is expensive to obtain. We present promising results on apparel and sporting goods products and show that our system can accurately extract attribute-value pairs from product descriptions. We describe a variety of application that are built on top of the results obtained by the attribute extraction system.
The trend toward ubiquitous computing does not represent simply a change in the way people access and use information. In the end it will have a profound effect on the way people access and use services, enabling new classes of services that only make sense by virtue of being embedded in the environment. Ultimately these technologies will lead us to a world of ubiquitous commerce. The prospect of ubiquitous computing, therefore, poses a fundamental question to businesses: What will it mean to conduct commerce in a world where our physical environments are teeming with services?Fundamentally, ubiquitous computing can and will change the way businesses and consumers are able to access each other. Gaining access to customers has been in the past a key challenge for businesses. What if accessing customers disappeared as a problem? Doesn't the rise of ubiquitous computing promise businesses the ability to deliver the right message to the right person at the right time at extremely low cost? Yet, these new and improved ways of reaching customers raise a whole new set of challenges that in many ways are far more complex than issues of cost.
This paper describes a prototype that predicts the shopping lists for customers in a retail store. The shopping list prediction is one aspect of a larger system we have developed for retailers to provide individual and personalized interactions with customers as they navigate through the retail store. Instead of using traditional personalization approaches, such as clustering or segmentation, we learn separate classifiers for each customer from historical transactional data. This allows us to make very fine-grained and accurate predictions about what items a particular individual customer will buy on a given shopping trip.We formally frame the shopping list prediction as a classification problem, describe the algorithms and methodology behind our system, its impact on the business case in which we frame it, and explore some of the properties of the data source that make it an interesting testbed for KDD algorithms. Our results show that we can predict a shopper's shopping list with high levels of accuracy, precision, and recall. We believe that this work impacts both the data mining and the retail business community. The formulation of shopping list prediction as a machine learning problem results in algorithms that should be useful beyond retail shopping list prediction. For retailers, the result is not only a practical system that increases revenues by up to 11%, but also enhances customer experience and loyalty by giving them the tools to individually interact with customers and anticipate their needs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.