In recent years, there has been significant interest in the development of ranking functions and efficient top-k retrieval algorithms to help users in ad-hoc search and retrieval in databases (e.g., buyers searching for products in a catalog). We introduce a complementary problem: how to guide a seller in selecting the best attributes of a new tuple (e.g., a new product) to highlight so that it stands out in the crowd of existing competitive products and is widely visible to the pool of potential buyers. We refer this problem as "attributes selection" problem. Package design based on user input is a problem that has also attracted recent interest. Given a set of elements, and a set of user preferences (where each preference is a conjunction of positive or negative preferences for individual elements), we investigate the problem of designing the most "popular package", i.e., a subset of the elements that maximizes the number of satisfied users. Numerous instances of this problem occur in practice. We refer this later problem as "package design" problem. We develop several formulations of both the problems. Even for the NP-complete problems, we give several exact (optimal) and approximation algorithms that work well in practice. Our experimental evaluation on real and synthetic datasets shows that the optimal and approximate algorithms are efficient for moderate and large datasets respectively, and also that the approximate algorithms have small approximation error.
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