Generating Groups of Products Using Graph Mining Techniques

AbstractRetail industry has evolved. Nowadays, companies around the world need a better and deeper understanding of their customers. In order to enhance store layout, generate customers groups, offers and personalized recommendations, among others. To accomplish these objectives, it is very important to know which products are related to each other.Classical approaches for clustering products, such as K-means or SOFM, do not work when exist scattered and large amounts of data. Even association rules give results that are difficult to interpret. These facts motivate us to use a novel approach that generates communities of products. One of the main advantages of these communities is that are meaningful and easily interpretable by retail analysts. This approach allows the processing of billions of transaction records within a reasonable time, according to the needs of companies

Synthesis of Attributed Feature Models From Product Descriptions: Foundations

Feature modeling is a widely used formalism to characterize a set of products (also called configurations). As a manual elaboration is a long and arduous task, numerous techniques have been proposed to reverse engineer feature models from various kinds of artefacts. But none of them synthesize feature attributes (or constraints over attributes) despite the practical relevance of attributes for documenting the different values across a range of products. In this report, we develop an algorithm for synthesizing attributed feature models given a set of product descriptions. We present sound, complete, and parametrizable techniques for computing all possible hierarchies, feature groups, placements of feature attributes, domain values, and constraints. We perform a complexity analysis w.r.t. number of features, attributes, configurations, and domain size. We also evaluate the scalability of our synthesis procedure using randomized configuration matrices. This report is a first step that aims to describe the foundations for synthesizing attributed feature models

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe