Interactive Exploration of Composite Items

Data exploration is seeing a renewed interest in our community. With the rise of big data analytics, this area is growing to encompass not only approaches and algorithms to find the next best data items to explore but also interactivity, i.e. accounting for feedback from the data scientist during the exploration. Interactivity is essential to account for evolving needs during the exploration and also customize the discovery process. In this tutorial, we focus on the interactive exploration of Composite Items (CIs). CIs address complex information needs and are prevalent in online shopping where products are bundled together to provide discounts, in travel itinerary recommendation where points of interest in a city are combined into a single travel package, and task assignment in crowdsourcing where persoalized micro-tasks are composed and recommended to workers. CI formation is usually expressed as a constrained optimization problem. For instance, in online shopping, package retrieval can retrieve the cheapest smartphones (optimization objective) with compatible accessories (constraints). Similarly, a city tour must be the most popular and conform to a total time and cost budget. A data scientist interested in exploring a variety of CIs has to repeatedly reformulate optimization problems with new constraints and objectives. In this tutorial, we investigate the applicability of interactive data exploration approaches to CI formation. We will first review CI applications and shapes (15mn). We then discuss three big research questions 60mn): (i) algorithms for CI formation, (ii) modes of exploration for CIs, and (iii) human-inthe-loop CIs. We will conclude with research directions (15mn). The proposed tutorial is timely. It brings together several related efforts and addresses unsolved questions in the emerging area of human-in-the-loop exploration of complex information needs. The tutorial is relevant to the general area of data science and more specifically to Scalable Analytics, Data Mining, Clustering and Knowledge Discovery, Indexing, Query Processing and Optimization, and Crowdsourcing. The technical topics covered are constrained optimization, ranking semantics, clustering, algorithms, and empirical evaluations.