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Determining Attributes to Maximize Visibility of Objects


Muhammed Miah, Student Member, IEEE, Gautam Das, Vagelis Hristidis, and Heikki Mannila

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Abstract—

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 develop several
formulations of this problem. Although the problems are NP-complete, we give several exact and approximation algorithms that work
well in practice. One type of exact algorithms is based on Integer Programming (IP) formulations of the problems. Another class of
exact methods is based on maximal frequent item set mining algorithms. The approximation algorithms are based on greedy
heuristics. A detailed performance study illustrates the benefits of our methods on real and synthetic data.