Quantifying the competitiveness of a dataset in relation to general preferences

Typically, a specific market (e.g., of hotels, restaurants, laptops, etc.) is represented as a multi-attribute dataset of the available products. The topic of identifying and shortlisting the products of most interest to a user has been well-explored. In contrast, in this work we focus on the dataset, and aim to assess its competitiveness with regard to different possible preferences. We define measures of competitiveness, and represent them in the form of a heat-map in the domain of preferences. Our work finds application in market analysis and in business development. These applications are further enhanced when the competitiveness heat-map is used in tandem with information on user preferences (which can be readily derived by existing methods). Interestingly, our study also finds side-applications with strong practical relevance in the area of multi-objective querying. We propose a suite of algorithms to efficiently produce the heat-map, and conduct case studies and an empirical evaluation to demonstrate the practicality of our work.